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"This problem is important for practical usage" "['non', 'non', 'non', 'non', 'non', 'non', 'non']" "paper quality"
"Minor things: + The main idea is described too sketchily" "['non', 'non', 'non', 'non', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg']" "paper quality"
"I think more examples, such as in section 8.1, should be put in the main text" "['non', 'non', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg']" "paper quality"
"The paper proposes a modular approach to the problem of mapping instructions to robot actions." "['non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non']" "paper quality"
"The first of two modules is responsible for learning a goal embedding of a given instruction using a learned distance function." "['non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non']" "paper quality"
"The second module is responsible for mapping goals from this embedding space to control policies." "['non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non']" "paper quality"
"STRENGTHS + Decoupling instruction-to-action mapping by introducing goals as a learned intermediate representation has advantages, particularly for goal-directed instructions" "['non', 'non', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg']" "paper quality"
"Notably, these together with the ability to train the components separately will generally increase the efficiency of learning" "['non', 'non', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg']" "paper quality"
"WEAKNESSES - The algorithmic contribution is relatively minor, while the technical merits of the approach are questionable" "['non', 'non', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg']" "paper quality"
"This is in contrast to semantic parsing and symbol grounding models, which exploit the compositionality of language to generalize to new instructions." "['non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non']" "paper quality"
"The trajectory encoder operates differently for goal-oriented vs. trajectory-oriented instructions, however it is not clear how a given instruction is identified as being goal- vs. trajectory-oriented" "['non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg']" "paper quality"
"While there are advantages to training the modules separately , there is a risk that they are reasoning over different portions of the goal space" "['non', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'non', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg']" "paper quality"
"A contrastive loss would seemingly be more appropriate for learning the instruction-goal distance function" "['arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg']" "paper quality"
"Are they free-form instructions" "['arg', 'arg', 'arg', 'arg', 'arg', 'arg']" "paper quality"
"How many are there" "['arg', 'arg', 'arg', 'arg']" "paper quality"
"Where do they come from" "['arg', 'arg', 'arg', 'arg', 'arg']" "paper quality"
"How different are the familiar and unfamiliar instructions" "['arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg']" "paper quality"
"Similarly, what is the nature of the different action spaces" "['non', 'non', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg']" "paper quality"
"The paper provides insufficient details regarding the RL and IL baselines, making it impossible to judge their merits" "['arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg']" "paper quality"
"I wouldn't consider the results reported in Section 4.5 to be ablative studies" "['arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg']" "paper quality"
"The paper incorrectly references Mei et al. 2016 when stating that methods require a large amount of human supervision (data annotation) and/or linguistic knowledge." "['arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non']" "paper quality"
"In fact Mei et al. 2016 requires no human annotation or linguistic knowledge." "['non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non']" "paper quality"
"There are several grammatical errors - The captions for Figures 3 and 4 are copied from Figure 1" "['arg', 'arg', 'arg', 'arg', 'arg', 'non', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg']" "paper quality"
"They used an attention mechanism over agent policies as an input to a central value function." "['non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non']" "paper quality"
"MAAC outperforms baselines on TC, but not on RT." "['non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non']" "paper quality"
"Furthermore, the different baselines perform differently: there is no method that consistently performs well." "['non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non']" "paper quality"
"Con - MAAC still requires all observations and actions of all other agents as an input to the value function, which makes this approach not scalable to settings with many agents" "['non', 'non', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg']" "paper quality"
"The centralized nature is also semantically improbable , as the observations might be high-dimensional in nature, so exchanging these between agents becomes impractical with complex problems." "['arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non']" "paper quality"
"MAAC does not consistently outperform baselines , and it is not clear how the stated explanations about the difference in performance apply to other problems" "['arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'non', 'non', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg']" "paper quality"
"It is unclear how the model actually operates and uses attention during execution" "['arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg']" "paper quality"
"Reproducibility - It seems straightforward to implement this method, but I encourage open-sourcing the authors' implementation." "['non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non']" "paper quality"
"Summary This paper proposes an evolutionary-based method for the multi-objective neural architecture search, where the proposed method aims at minimizing two objectives: an error metric and the number of FLOPS." "['non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non']" "paper quality"
"The proposed method consists of an exploration step and an exploitation step." "['non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non']" "paper quality"
"In the exploration step, architectures are sampled by using genetic operators such as the crossover and the mutation." "['non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non']" "paper quality"
"In the exploitation step, architectures are generated by a Bayesian Network." "['non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non']" "paper quality"
"Pros - The performance of the proposed method is better than the existing multi-objective architecture search methods in the object classification task" "['non', 'non', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg']" "paper quality"
"The effect of each proposed technique is appropriately evaluated" "['arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg']" "paper quality"
"The proposed method is compared with the existing multi-objective methods in terms of classification accuracy, but if we focus on that point, the performance (i.e., error rate and FLOPs) of the proposed method is almost the same as those of the random search judging from Table 4" "['arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg']" "paper quality"
"It would be better to compare the proposed method to the existing multi-objective methods in terms of classification accuracy and other objectives" "['arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg']" "paper quality"
"This paper argues that the choice of the number of parameters is sub-optimal and ineffective in terms of computational complexity." "['non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non']" "paper quality"
"Please provide more details about this point." "['non', 'non', 'non', 'non', 'non', 'non', 'non', 'non']" "paper quality"
"For example, what is the drawbacks of the number of parameters, what is the advantages of FLOPs for multi-objective optimization" "['non', 'non', 'non', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg']" "paper quality"
"Please elaborate on the procedure and settings of the Bayesian network used in this paper" "['arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg']" "paper quality"
"This paper tackles the problem of catastrophic forgetting when data is organized in a large number of batches of data (tasks) that are sequentially made available." "['non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non']" "paper quality"
"To avoid catastrophic forgetting, the authors learn a VAE that generates the training data (both inputs and labels) and retrain it using samples from the new task combined with samples generated from the VAE trained in the previous tasks (generative replay)." "['non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non']" "paper quality"
"I like that this paper uses a single global probabilistic model instead of separate discriminative and generative ones" "['arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg']" "paper quality"
"Unfortunately, there are several things that left me unconvinced about this paper: 1) Presentation of the paper - Variables x, y, z are introduced and talked about without explanation" "['non', 'non', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg']" "paper quality"
"The graphical model or factorization assumptions are not even mentioned until after the loss has been defined" "['arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg', 'arg']" "paper quality"
"A normal flow is to first describe the model and what the involved variables mean, and then talk about what the loss for learning it should be, not the other way around." "['non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non']" "paper quality"
"Text contradicting the equation : ""In order to balance the individual loss terms, we normalize according to dimensions and weight the KL divergence with a constant of 0.1""." "['arg', 'arg', 'arg', 'arg', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non', 'non']" "paper quality"