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⌀ | empirical_novelty
stringclasses 763
values |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
null |
Facebook AI Research, New York, USA
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Natural language processing
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6 |
5;6;7
| null | null |
Dialogue Learning With Human-in-the-Loop
| null | null | 0 | 3.666667 |
Poster
|
4;3;4
| null |
null |
AGH University of Science and Technology, Department of Computer Science, Krakow, Poland
|
2017
| 0 | null | null | 0 | null | null | null | null | null |
Karol Grzegorczyk & Marcin Kurdziel, AGH University of Science and Technology , Department of Computer Science , Krakow, Poland , {kgr,kurdziel}@agh.edu.pl
| null |
Natural language processing;Transfer Learning
| null | 0 | null | null |
iclr
| -0.944911 | 0 | null |
main
| 5.666667 |
5;6;6
| null | null |
Binary Paragraph Vectors
| null | null | 0 | 3.333333 |
Reject
|
5;3;2
| null |
null |
Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Microsoft Research, Cambridge, CB1 2FB, UK
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep learning;Semi-Supervised Learning
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 6 |
4;7;7
| null | null |
Neural Program Lattices
| null | null | 0 | 4.333333 |
Poster
|
4;4;5
| null |
null |
Tsinghua National Laboratory for Information Science and Technology, Department of Electronic Engineering, Tsinghua University, Beijing, China; Department of Electronic Engineering, Tsinghua University, Beijing, China
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Natural language processing;Deep learning;Applications
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 3.333333 |
3;3;4
| null | null |
Learning to Understand: Incorporating Local Contexts with Global Attention for Sentiment Classification
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null |
Department of Electrical and Computer Engineering, University of Illinois at Urbana Champaign, Urbana, IL 61801, USA
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Natural language processing
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 7 |
7;7;7
| null | null |
Geometry of Polysemy
| null | null | 0 | 3.666667 |
Poster
|
4;3;4
| null |
null |
School of Computer Science, McGill University, Montreal, QC, Canada
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Reinforcement Learning;Deep learning
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 3.666667 |
3;4;4
| null | null |
Investigating Recurrence and Eligibility Traces in Deep Q-Networks
| null | null | 0 | 4.666667 |
Reject
|
5;5;4
| null |
null |
College of Computer Science, Northeastern University, MA 02115, USA; Microsoft Research, WA 98052, USA; Department of Engineering Science, University of Oxford, Oxford OX13PJ, UK; Department of Psychology, Stanford University, CA 94305, USA
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Semi-Supervised Learning;Deep learning;Computer vision
| null | 0 | null | null |
iclr
| 0.866025 | 0 | null |
main
| 5.666667 |
5;6;6
| null | null |
Learning Disentangled Representations in Deep Generative Models
| null | null | 0 | 4 |
Reject
|
3;4;5
| null |
null |
Faculty of Mathematics, Informatics and Mechanics, University of Warsaw, Poland; Electrical and Computer Engineering, Purdue University, USA
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4 |
3;4;4;5
| null | null |
ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation
| null | null | 0 | 4 |
Reject
|
4;4;4;4
| null |
null |
Department of Computer Science, Aalto University, Finland; School of Information Sciences, University of Tampere, Finland
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Unsupervised Learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4 |
4;4;4
| null | null |
An Information Retrieval Approach for Finding Dependent Subspaces of Multiple Views
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null |
Microsoft AI & Research
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6 |
6;6;6
| null | null |
MS MARCO: A Human-Generated MAchine Reading COmprehension Dataset
| null | null | 0 | 2 |
Reject
|
3;0;3
| null |
null |
LMNO UMR CNRS, Statistics and Data Science, University of Caen, Caen, France; DYNI, LSIS UMR CNRS, Machine Learning, AMU, University of Toulon, ENSAM, La Garde, France
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 4.666667 |
4;5;5
| null | null |
BIOACOUSTIC SEGMENTATION BY HIERARCHICAL DIRICHLET PROCESS HIDDEN MARKOV MODEL
| null | null | 0 | 3.666667 |
Reject
|
3;3;5
| null |
null |
Facebook AI Research; Carnegie Mellon University
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Reinforcement Learning;Applications;Games
| null | 0 | null | null |
iclr
| -0.944911 | 0 | null |
main
| 5.666667 |
4;6;7
| null | null |
Training Agent for First-Person Shooter Game with Actor-Critic Curriculum Learning
| null | null | 0 | 4.333333 |
Poster
|
5;4;4
| null |
null |
Facebook AI Research; Courant Institute of Mathematical Sciences
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| 0.654654 | 0 | null |
main
| 8.333333 |
7;8;10
| null | null |
Towards Principled Methods for Training Generative Adversarial Networks
| null | null | 0 | 4 |
Oral
|
4;3;5
| null |
null |
School of Informatics, University of Edinburgh, Edinburgh, Scotland, UK; Toyota Technological Institute at Chicago, Chicago, Illinois, USA
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep learning;Natural language processing;Unsupervised Learning
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 4.666667 |
4;4;6
| null | null |
Multiplicative LSTM for sequence modelling
| null | null | 0 | 4.333333 |
Workshop
|
5;4;4
| null |
null |
Oregon State University, Kelley Engineering Center, Corvallis, OR, 97331; IBM Watson, Yorktown Heights, NY 10598 USA
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Natural language processing;Supervised Learning;Applications;Deep learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4.666667 |
4;4;6
| null | null |
Classify or Select: Neural Architectures for Extractive Document Summarization
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null |
Facebook AI Research
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Theory;Deep learning;Optimization
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 5.333333 |
4;4;8
| null | null |
Symmetry-Breaking Convergence Analysis of Certain Two-layered Neural Networks with ReLU nonlinearity
| null | null | 0 | 3.666667 |
Workshop
|
4;3;4
| null |
null |
Facebook AI Research; Google DeepMind; Facebook AI Research, University of Trento
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Natural language processing;Reinforcement Learning;Games
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 7 |
7;7;7
| null | null |
Multi-Agent Cooperation and the Emergence of (Natural) Language
| null | null | 0 | 3 |
Oral
|
3;3;3
| null |
null |
University of Massachusetts Medical School, Bedford V AMC
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Natural language processing;Deep learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6.666667 |
6;7;7
| null | null |
Reasoning with Memory Augmented Neural Networks for Language Comprehension
| null | null | 0 | 3 |
Poster
|
3;4;2
| null |
null |
The University of Tokyo, Bunkyo-ku, Tokyo, Japan
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4.333333 |
3;5;5
| null | null |
Joint Multimodal Learning with Deep Generative Models
| null | null | 0 | 4 |
Reject
|
4;5;3
| null |
null |
Stanford University
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Natural language processing;Deep learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5.333333 |
5;5;6
| null | null |
Deep Biaffine Attention for Neural Dependency Parsing
| null | null | 0 | 4 |
Poster
|
4;4;4
| null |
null |
University of Toronto & Google Brain; Google Brain
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep learning;Supervised Learning;Speech;Structured prediction
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5.333333 |
5;5;6
| null | null |
Regularizing Neural Networks by Penalizing Confident Output Distributions
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null |
School of Engineering and Applied Sciences, Harvard University, Cambridge, MA 02138, USA
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 8 |
8;8;8
| null | null |
Structured Attention Networks
| null | null | 0 | 4 |
Poster
|
5;3;4
| null |
null |
Department of Neurosurgery and Hansen Experimental Physics Laboratory, Stanford University; Santa Cruz Institute for Particle Physics, University of California, Santa Cruz; Department of Physics, Stanford University; Neurosciences Graduate Program, University of California, San Diego; Departments of Statistics and Neuroscience, Columbia University; Doctoral Program in Neurobiology & Behavior, Columbia University
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep learning;Applications
| null | 0 | null | null |
iclr
| 0.693375 | 0 | null |
main
| 6.333333 |
4;7;8
| null | null |
Multilayer Recurrent Network Models of Primate Retinal Ganglion Cell Responses
| null | null | 0 | 4.333333 |
Poster
|
4;4;5
| null |
null |
Google, New York, NY; University of Washington, Seattle, WA
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Natural language processing
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6.333333 |
6;6;7
| null | null |
Learning Recurrent Span Representations for Extractive Question Answering
| null | null | 0 | 4 |
Reject
|
3;5;4
| null |
null |
Department of Computer Science, University College London, London, UK
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6 |
5;6;7
| null | null |
Programming With a Differentiable Forth Interpreter
| null | null | 0 | 2.666667 |
Workshop
|
2;4;2
| null |
null |
UC Berkeley, Department of Electrical Engineering and Computer Science; OpenAI
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Reinforcement Learning;Deep learning
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 3.333333 |
3;3;4
| null | null |
RL^2: Fast Reinforcement Learning via Slow Reinforcement Learning
| null | null | 0 | 3.666667 |
Reject
|
4;4;3
| null |
null |
Department of Computer Science, University of California, Irvine, Irvine, CA 92697 USA
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5.333333 |
5;5;6
| null | null |
Energy-Based Spherical Sparse Coding
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null |
School of Engineering and Physical Sciences, Heriot-Watt University, Edinburgh, UK; Renishaw plc, Research Ave, North, Edinburgh, UK
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Computer vision;Unsupervised Learning;Applications
| null | 0 | null | null |
iclr
| 1 | 0 | null |
main
| 5.666667 |
5;6;6
| null | null |
Neural Photo Editing with Introspective Adversarial Networks
| null | null | 0 | 3.666667 |
Poster
|
3;4;4
| null |
null |
QUV A Lab, Informatics Institute, University of Amsterdam, Amsterdam, Netherlands
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Computer vision;Deep learning;Applications
| null | 0 | null | null |
iclr
| 1 | 0 | null |
main
| 7.333333 |
6;8;8
| null | null |
Sigma Delta Quantized Networks
| null | null | 0 | 3.666667 |
Poster
|
3;4;4
| null |
null |
Informatics Institute, University of Amsterdam
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Optimization;Deep learning;Computer vision
| null | 0 | null | null |
iclr
| -0.944911 | 0 | null |
main
| 5.333333 |
4;5;7
| null | null |
Recurrent Inference Machines for Solving Inverse Problems
| null | null | 0 | 3.666667 |
Reject
|
4;4;3
| null |
null |
Computer Science and Artificial Intelligence Lab, MIT
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Natural language processing;Supervised Learning;Structured prediction
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6.333333 |
6;6;7
| null | null |
Tree-structured decoding with doubly-recurrent neural networks
| null | null | 0 | 4 |
Poster
|
4;4;4
| null |
null |
School of Computer Science, Carnegie Mellon University
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Natural language processing;Deep learning;Supervised Learning
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 6.333333 |
6;6;7
| null | null |
Gated-Attention Readers for Text Comprehension
|
https://github.com/bdhingra/ga-reader
| null | 0 | 2 |
Reject
|
0;3;3
| null |
null |
University of California, Berkeley; Toyota Technological Institute at Chicago
|
2017
| 0 | null | null | 0 | null | null | null | null | null |
Dan Hendrycks and Kevin Gimpel
| null |
Computer vision
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6 |
6;6;6
| null | null |
A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks
|
github.com/hendrycks/error-detection
| null | 0 | 3 |
Poster
|
3;3;3
| null |
null |
Adobe Systems Inc, Noida, Uttar Pradesh, India; Department of Computer Science, IIT Kanpur, Uttar Pradesh, India; Department of Electronics and Electrical Comm. Engg., IIT Kharagpur, West Bengal, India
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Computer vision;Deep learning;Optimization
| null | 0 | null | null |
iclr
| 0.866025 | 0 | null |
main
| 8 |
7;8;9
| null | null |
Introspection:Accelerating Neural Network Training By Learning Weight Evolution
| null | null | 0 | 4.666667 |
Poster
|
4;5;5
| null |
null |
Snap Research; Microsoft Research; Beckman Institute, University of Illinois at Urbana-Champaign
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| 1 | 0 | null |
main
| 6.666667 |
6;7;7
| null | null |
Support Regularized Sparse Coding and Its Fast Encoder
| null | null | 0 | 3.666667 |
Poster
|
3;4;4
| null |
null |
Massachusetts Institute of Technology; Microsoft Research
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Supervised Learning
| null | 0 | null | null |
iclr
| -0.480384 | 0 | null |
main
| 5.4 |
4;5;5;6;7
| null | null |
Neural Functional Programming
| null | null | 0 | 2.6 |
Workshop
|
3;2;3;3;2
| null |
null |
Google Brain; Montreal Institute for Learning Algorithms, University of Montreal, Montreal, QC H3T1J4
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep learning;Unsupervised Learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 7.666667 |
7;8;8
| null | null |
Density estimation using Real NVP
| null | null | 0 | 4 |
Poster
|
4;4;4
| null |
null |
University of Texas at Austin, Austin, TX, USA; Carnegie Mellon University, Pittsburgh, PA, USA; Google, Mountain View, CA, USA
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6 |
6;6;6
| null | null |
Joint Training of Ratings and Reviews with Recurrent Recommender Networks
| null | null | 0 | 3.666667 |
Reject
|
4;3;4
| null |
null |
Department of EE, CSE, University of Washington; Department of GS, CSE, University of Washington; Department of Statistics, University of Washington; Department of CSE, University of Washington
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep learning
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 7 |
6;6;9
| null | null |
Training Compressed Fully-Connected Networks with a Density-Diversity Penalty
| null | null | 0 | 3.333333 |
Poster
|
4;2;4
| null |
null |
Ecole Polytechnique de Montreal, Montreal, Canada; Montreal Institute for Learning Algorithms, Montreal, Canada; Ecole Polytechnique de Montreal, Montreal, Canada; CHUM Research Center, Montreal, Canada
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep learning
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 5.666667 |
5;5;7
| null | null |
On orthogonality and learning recurrent networks with long term dependencies
| null | null | 0 | 4.333333 |
Reject
|
4;5;4
| null |
null |
Department of Electrical Engineering and Computer Science, Seoul National University, Gwanak-Gu, 08826, Republic of Korea
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 5 |
4;5;6
| null | null |
Coarse Pruning of Convolutional Neural Networks with Random Masks
| null | null | 0 | 3.666667 |
Reject
|
4;4;3
| null |
null |
Google; Pennsylvania State University; Google Brain
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| 1 | 0 | null |
main
| 8.333333 |
7;9;9
| null | null |
Semi-supervised Knowledge Transfer for Deep Learning from Private Training Data
| null | null | 0 | 3.666667 |
Oral
|
3;4;4
| null |
null |
Facebook AI Research
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Computer vision;Unsupervised Learning
| null | 0 | null | null |
iclr
| 0.755929 | 0 |
http://joo.st/ICLR/GenerationBenchmark
|
main
| 4.666667 |
3;5;6
| null | null |
Transformation-based Models of Video Sequences
| null | null | 0 | 3.333333 |
Reject
|
3;3;4
| null |
null |
Computer Science Department, Bar-Ilan University, Ramat-Gan, Israel
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Natural language processing;Applications
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 4.666667 |
4;5;5
| null | null |
Sequence to Sequence Transduction with Hard Monotonic Attention
| null | null | 0 | 4 |
Reject
|
5;4;3
| null |
null |
Department of Computer Science and Technology, Ocean University of China; Department of International Trade and Economy, Ocean University of China
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 3 |
3;3;3
| null | null |
Deep Error-Correcting Output Codes
| null | null | 0 | 4.666667 |
Reject
|
5;4;5
| null |
null |
Innovative Computing Laboratory, The University of Tennessee, Knoxville; Big Data Research Center, Univ. of Electr. Sci. & Tech. of China; School of Computational Science & Engineering, Georgia Institute of Technology; School of Computer Science, Georgia Institute of Technology
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Applications;Deep learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5 |
5;5
| null | null |
Efficient Communications in Training Large Scale Neural Networks
| null | null | 0 | 4 |
Reject
|
3;5
| null |
null |
Dept. of Computer Science, University of Houston; Instituto Nacional de Astrofisica, Optica y Electronica, Computer Science Department; Dept. of Computing Systems and Industrial Engineering, Universidad Nacional de Colombia
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Multi-modal learning;Applications;Supervised Learning
| null | 0 | null | null |
iclr
| 0.327327 | 0 | null |
main
| 5.666667 |
4;6;7
| null | null |
Gated Multimodal Units for Information Fusion
| null | null | 0 | 4 |
Workshop
|
4;3;5
| null |
null |
Electrical and Computer Engineering, University of Toronto, Toronto, ON, M5S 3G4, Canada
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep learning;Applications
| null | 0 | null | null |
iclr
| 0.200446 | 0 | null |
main
| 4.8 |
4;4;5;5;6
| null | null |
Tartan: Accelerating Fully-Connected and Convolutional Layers in Deep Learning Networks by Exploiting Numerical Precision Variability
| null | null | 0 | 3.2 |
Reject
|
1;3;5;5;2
| null |
null |
Google Research, Google Brain, Google DeepMind; Department of Electrical Engineering and Computer Science, University of California, Berkeley
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 3.333333 |
3;3;4
| null | null |
Tree-Structured Variational Autoencoder
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null |
Courant Institute of Mathematical Sciences, New York University, New York, NY 10012, USA
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep learning
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 3.666667 |
3;4;4
| null | null |
Divide and Conquer with Neural Networks
| null | null | 0 | 2.666667 |
Reject
|
2;4;2
| null |
null |
Google Brain; Google Research; Adobe Research; Columbia University
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| 0 | 0 |
http://edwardlib.org/iclr2017
|
main
| 6.666667 |
5;7;8
| null | null |
Deep Probabilistic Programming
| null | null | 0 | 4 |
Poster
|
4;4;4
| null |
null |
Bosch Research and Technology Center, Palo Alto, CA and University of Illinois at Chicago, Chicago, IL; Bosch Research and Technology Center, Palo Alto, CA; Bosch Research and Technology Center, Palo Alto, CA and Simon Fraser University, Burnaby, BC
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4 |
3;4;5
| null | null |
Deep Symbolic Representation Learning for Heterogeneous Time-series Classification
| null | null | 0 | 3.666667 |
Reject
|
4;3;4
| null |
null |
Criteo Research
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| 0.866025 | 0 | null |
main
| 6 |
4;6;8
| null | null |
Tighter bounds lead to improved classifiers
| null | null | 0 | 4.333333 |
Poster
|
4;4;5
| null |
null |
OpenAI, San Francisco, CA, USA; Google Brain, Mountain View, CA, USA; Google Brain, Mountain View, CA, USA and UC Berkeley, Berkeley, CA, USA
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Optimization;Deep learning;Applications
| null | 0 | null | null |
iclr
| 1 | 0 | null |
main
| 5.666667 |
5;6;6
| null | null |
Revisiting Distributed Synchronous SGD
| null | null | 0 | 3.666667 |
Reject
|
3;4;4
| null |
null |
National Research University Higher School of Economics (HSE), Yandex; National Research University Higher School of Economics (HSE)
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep learning;Unsupervised Learning
| null | 0 | null | null |
iclr
| 0.188982 | 0 | null |
main
| 5.333333 |
4;5;7
| null | null |
Fast Adaptation in Generative Models with Generative Matching Networks
| null | null | 0 | 3.666667 |
Reject
|
4;3;4
| null |
null |
Computer Science Department, New York University; Mathematics Department, University of California, Irvine; Mathematics Department, New York University
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Optimization
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 4 |
2;5;5
| null | null |
Universality in halting time
| null | null | 0 | 3.666667 |
Reject
|
4;3;4
| null |
null |
POSTECH, Korea; Adobe Research
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep learning;Computer vision;Multi-modal learning
| null | 0 | null | null |
iclr
| -0.654654 | 0 | null |
main
| 5.666667 |
4;6;7
| null | null |
Progressive Attention Networks for Visual Attribute Prediction
| null | null | 0 | 4 |
Reject
|
5;3;4
| null |
null |
IBM T.J. Watson Research Center, Yorktown Heights, NY 10598 USA
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4.333333 |
4;4;5
| null | null |
Machine Solver for Physics Word Problems
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null |
Department of Computer Science and Operations Research, University of Montreal
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Computer vision;Deep learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6.333333 |
5;7;7
| null | null |
Incorporating long-range consistency in CNN-based texture generation
| null | null | 0 | 5 |
Poster
|
5;5;5
| null |
null |
DeepMind, CIFAR, Oxford University; DeepMind
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep learning;Reinforcement Learning
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 6.333333 |
6;6;7
| null | null |
Sample Efficient Actor-Critic with Experience Replay
| null | null | 0 | 3.333333 |
Poster
|
3;4;3
| null |
null |
University College London, UK; MediaGamma Ltd, UK; Shanghai Jiao Tong University, Shanghai, China
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Unsupervised Learning;Deep learning;Applications
| null | 0 | null | null |
iclr
| 1 | 0 | null |
main
| 4.333333 |
4;4;5
| null | null |
Cat2Vec: Learning Distributed Representation of Multi-field Categorical Data
| null | null | 0 | 4.333333 |
Reject
|
4;4;5
| null |
null |
Intel Labs China
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep learning;Optimization
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 7.333333 |
7;7;8
| null | null |
Incremental Network Quantization: Towards Lossless CNNs with Low-precision Weights
| null | null | 0 | 3.666667 |
Poster
|
3;4;4
| null |
null |
Department of Information and Communication Engineering, The University of Tokyo
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Computer vision;Deep learning
| null | 0 | null | null |
iclr
| -0.755929 | 0 | null |
main
| 5.333333 |
4;5;7
| null | null |
Significance of Softmax-Based Features over Metric Learning-Based Features
| null | null | 0 | 4.333333 |
Reject
|
5;4;4
| null |
null |
Data Lab, Volkswagen Group, 80805, München, Germany
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep learning;Unsupervised Learning
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 6.333333 |
6;6;7
| null | null |
Deep Variational Bayes Filters: Unsupervised Learning of State Space Models from Raw Data
| null | null | 0 | 3.333333 |
Poster
|
3;4;3
| null |
null |
Machine Intelligence Lab., SK Planet, Seongnam City, South Korea; Naver Labs, Naver Corp., Seongnam City, South Korea
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Computer vision;Deep learning;Supervised Learning;Applications
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 3.333333 |
3;3;4
| null | null |
Multi-label learning with the RNNs for Fashion Search
| null | null | 0 | 3.666667 |
Reject
|
4;3;4
| null |
null |
School of Mathematics and Statistics, Victoria University of Wellington, Wellington, New Zealand; Disney Research, Zurich, Switzerland; School of Engineering and Computer Science, Victoria University of Wellington, Wellington, New Zealand
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep learning;Optimization;Theory;Supervised Learning
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 5.666667 |
3;7;7
| null | null |
Neural Taylor Approximations: Convergence and Exploration in Rectifier Networks
| null | null | 0 | 3 |
Workshop
|
4;3;2
| null |
null |
College of Computing, Georgia Institute of Technology, Atlanta, GA 30332, USA
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep learning;Applications
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6 |
6;6;6
| null | null |
Recurrent Coevolutionary Feature Embedding Processes for Recommendation
| null | null | 0 | 3.666667 |
Reject
|
3;4;4
| null |
null |
Department of Computer Science, Stanford University
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Theory;Deep learning;Unsupervised Learning
| null | 0 | null | null |
iclr
| 0 | 0 |
https://arxiv.org/pdf/1702.08484.pdf
|
main
| 5.333333 |
5;5;6
| null | null |
Boosted Generative Models
| null | null | 0 | 3 |
Reject
|
3;3;3
| null |
null |
Baidu, Inc., China
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| 0.755929 | 0 | null |
main
| 5 |
4;5;6
| null | null |
HFH: Homologically Functional Hashing for Compressing Deep Neural Networks
| null | null | 0 | 3 |
Reject
|
0;5;4
| null |
null |
Center for Language and Speech Processing, Johns Hopkins University, Baltimore, MD 21218, USA
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Unsupervised Learning;Deep learning;Multi-modal learning
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 6 |
5;6;7
| null | null |
Deep Generalized Canonical Correlation Analysis
| null | null | 0 | 4 |
Reject
|
5;4;3
| null |
null |
IBM Thomas J. Watson Research Center, Yorktown Heights, NY USA; The Department of Computer Science, University of Southern California, Los Angeles, CA USA
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Unsupervised Learning;Computer vision;Transfer Learning;Optimization;Applications
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 5.666667 |
5;5;7
| null | null |
NEUROGENESIS-INSPIRED DICTIONARY LEARNING: ONLINE MODEL ADAPTION IN A CHANGING WORLD
| null | null | 0 | 3.666667 |
Reject
|
4;3;4
| null |
null |
IBM T.J. Watson Research Center, Yorktown Heights, NY, USA
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep learning;Transfer Learning;Natural language processing
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6 |
5;6;7
| null | null |
Representation Stability as a Regularizer for Improved Text Analytics Transfer Learning
| null | null | 0 | 3.666667 |
Reject
|
4;3;4
| null |
null |
Stanford University; Baidu Research; NVIDIA; Facebook
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep learning
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 7 |
5;8;8
| null | null |
DSD: Dense-Sparse-Dense Training for Deep Neural Networks
|
https://songhan.github.io/DSD
| null | 0 | 3.333333 |
Poster
|
4;3;3
| null |
null |
UC Berkeley, Department of Electrical Engineering and Computer Science, OpenAI; UC Berkeley, Department of Electrical Engineering and Computer Science
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep learning;Reinforcement Learning;Transfer Learning
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 6.333333 |
6;6;7
| null | null |
Learning Invariant Feature Spaces to Transfer Skills with Reinforcement Learning
| null | null | 0 | 4 |
Poster
|
4;5;3
| null |
null |
Department of Electrical Engineering, Technion, Israel Institute of Technology
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Reinforcement Learning;Deep learning;Games
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5.333333 |
4;5;7
| null | null |
Playing SNES in the Retro Learning Environment
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null |
Google DeepMind
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep learning;Computer vision;Multi-modal learning;Natural language processing
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6 |
5;6;7
| null | null |
Generating Interpretable Images with Controllable Structure
| null | null | 0 | 3 |
Workshop
|
3;3;3
| null |
null |
Google Research
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Theory;Computer vision;Deep learning;Supervised Learning
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 6.333333 |
6;6;7
| null | null |
Deep Variational Information Bottleneck
| null | null | 0 | 3.666667 |
Poster
|
4;3;4
| null |
null |
Department of Computer Science, University of Freiburg
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep learning;Applications
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 7 |
7;7;7
| null | null |
Learning Curve Prediction with Bayesian Neural Networks
| null | null | 0 | 4.333333 |
Poster
|
4;5;4
| null |
null |
1Skolkovo Institute of Science and Technology, Moscow, Russia; 2Yandex LLC, Moscow, Russia; 1Skolkovo Institute of Science and Technology, Moscow, Russia; 4Institute of Numerical Mathematics, Russian Academy of Sciences, Moscow, Russia
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Natural language processing;Unsupervised Learning
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 5 |
4;5;6
| null | null |
Riemannian Optimization for Skip-Gram Negative Sampling
| null | null | 0 | 3.333333 |
Reject
|
4;3;3
| null |
null |
EPFL, Switzerland
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Structured prediction
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4 |
4;4;4
| null | null |
Structured Sequence Modeling with Graph Convolutional Recurrent Networks
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null |
Department of EECS, University of Michigan, Ann Arbor, MI 48109, USA
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Natural language processing;Deep learning;Applications
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 6.333333 |
6;6;7
| null | null |
Sentence Ordering using Recurrent Neural Networks
| null | null | 0 | 3.666667 |
Reject
|
4;4;3
| null |
null |
Facebook AI Research, New York, USA
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 7.666667 |
7;8;8
| null | null |
Learning End-to-End Goal-Oriented Dialog
| null | null | 0 | 4.333333 |
Oral
|
4;4;5
| null |
null |
Department of Computer Science & Engineering, University of Washington, Seattle, WA 98195, USA
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Computer vision;Supervised Learning
| null | 0 | null | null |
iclr
| 0.333333 | 0 | null |
main
| 5.25 |
5;5;5;6
| null | null |
Compositional Kernel Machines
| null | null | 0 | 3.75 |
Workshop
|
4;4;3;4
| null |
null |
Google Brain, Visiting from Carnegie Mellon University; Google Brain
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 5 |
4;5;6
| null | null |
Learning to Protect Communications with Adversarial Neural Cryptography
| null | null | 0 | 3 |
Reject
|
2;4;3
| null |
null |
The Robotics Institute, School of Computer Science, Carnegie Mellon University, Pittsburgh, P.A., 15213
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4 |
4;4;4
| null | null |
OMG: Orthogonal Method of Grouping With Application of K-Shot Learning
| null | null | 0 | 4.333333 |
Reject
|
4;5;4
| null |
null |
IBM Research, San Jose, CA 95120, USA
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Unsupervised Learning;Applications;Deep learning
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 3.333333 |
3;3;4
| null | null |
Surprisal-Driven Feedback in Recurrent Networks
| null | null | 0 | 4.666667 |
Reject
|
5;5;4
| null |
null |
The University of Tokyo
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep learning;Unsupervised Learning
| null | 0 | null | null |
iclr
| 0.866025 | 0 | null |
main
| 5 |
4;5;6
| null | null |
b-GAN: Unified Framework of Generative Adversarial Networks
| null | null | 0 | 3.333333 |
Reject
|
3;3;4
| null |
null |
Oracle Labs, Burlington, MA; Carnegie Mellon University, Pittsburgh, PA
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Natural language processing;Structured prediction;Deep learning
| null | 0 | null | null |
iclr
| 1 | 0 | null |
main
| 3.333333 |
3;3;4
| null | null |
Enforcing constraints on outputs with unconstrained inference
| null | null | 0 | 4.333333 |
Reject
|
4;4;5
| null |
null |
Facebook AI Research, New York, NY 10003, USA; Department of Computer Science, New York University, New York, NY 10012, USA
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Natural language processing;Deep learning
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 6 |
4;7;7
| null | null |
Variable Computation in Recurrent Neural Networks
| null | null | 0 | 4.333333 |
Poster
|
5;4;4
| null |
null |
OpenAI; Google Brain
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Supervised Learning;Computer vision
| null | 0 | null | null |
iclr
| -1 | 0 |
https://youtu.be/zQ_uMenoBCk
|
main
| 5.666667 |
5;6;6
| null | null |
Adversarial examples in the physical world
| null | null | 0 | 3.333333 |
Workshop
|
4;3;3
| null |
null |
David R. Cheriton School of Computer Science, University of Waterloo, Waterloo, Ontario, Canada
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Unsupervised Learning;Deep learning
| null | 0 | null | null |
iclr
| 0.866025 | 0 | null |
main
| 4.666667 |
4;4;6
| null | null |
Online Structure Learning for Sum-Product Networks with Gaussian Leaves
| null | null | 0 | 2 |
Workshop
|
1;2;3
| null |
null |
Twitter, London, UK; Twitter, London, UK and University of Copenhagen, Denmark
|
2017
| 0 | null | null | 0 | null | null | null | null | null |
Casper Kaae Sonderby, Jose Caballero, Lucas Theis, Wenzhe Shi and Ferenc Huszar
| null |
Theory;Computer vision;Deep learning
| null | 0 | null | null |
iclr
| 0.327327 | 0 | null |
main
| 8 |
7;8;9
| null | null |
Amortised MAP Inference for Image Super-resolution
| null | null | 0 | 3.333333 |
Oral
|
2;5;3
| null |
null |
School of Electrical Engineering and Computer Science, Oregon State University, Corvallis, OR 97331, USA
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 6.666667 |
6;7;7
| null | null |
FILTER SHAPING FOR CONVOLUTIONAL NEURAL NETWORKS
| null | null | 0 | 3.666667 |
Poster
|
4;4;3
| null |
null |
University of Chicago; TTI Chicago
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 5.666667 |
5;6;6
| null | null |
FractalNet: Ultra-Deep Neural Networks without Residuals
| null | null | 0 | 4.666667 |
Poster
|
5;4;5
| null |
null |
Montreal Institute for Learning Algorithms, Universit ´e de Montr ´eal, CIFAR Senior Fellow; Montreal Institute for Learning Algorithms, Universit ´e de Montr ´eal
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep learning;Unsupervised Learning
| null | 0 | null | null |
iclr
| 0.944911 | 0 | null |
main
| 6.666667 |
6;7;7
| null | null |
Improving Generative Adversarial Networks with Denoising Feature Matching
| null | null | 0 | 3.666667 |
Poster
|
2;4;5
| null |
null |
Department of Information Engineering, Universit`a degli Studi di Firenze; Department of Computer Science, Katholieke Universiteit Leuven
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Supervised Learning
| null | 0 | null | null |
iclr
| 1 | 0 | null |
main
| 4.333333 |
3;5;5
| null | null |
Shift Aggregate Extract Networks
| null | null | 0 | 2.666667 |
Workshop
|
2;3;3
| null |
null |
Stanford University; Stanford University, NVIDIA; Tsinghua University
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep learning
| null | 0 | null | null |
iclr
| 0.650945 | 0 | null |
main
| 6.25 |
3;7;7;8
| null | null |
Trained Ternary Quantization
| null | null | 0 | 4 |
Poster
|
3;5;3;5
| null |
null |
Media Laboratory, Massachusetts Institute of Technology, Cambridge MA 02139, USA
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep learning;Reinforcement Learning
| null | 0 | null | null |
iclr
| 0 | 0 |
https://bowenbaker.github.io/metaqnn/
|
main
| 6 |
6;6;6
| null | null |
Designing Neural Network Architectures using Reinforcement Learning
|
https://github.com/bowenbaker/metaqnn
| null | 0 | 3.666667 |
Poster
|
4;3;4
| null |
null |
Department of Mathematics, California State University, Long Beach, CA 90840, USA; Department of Mathematics, Loyola Marymount University, Los Angeles, CA 90045, USA
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 7.333333 |
7;7;8
| null | null |
A recurrent neural network without chaos
| null | null | 0 | 3.666667 |
Poster
|
4;4;3
| null |
null |
Département d’informatique et de recherche opérationnelle, Université de Montréal
|
2017
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Natural language processing;Deep learning
| null | 0 | null | null |
iclr
| 1 | 0 | null |
main
| 7.666667 |
7;8;8
| null | null |
Hierarchical Multiscale Recurrent Neural Networks
| null | null | 0 | 3.666667 |
Poster
|
3;4;4
| null |
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