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values | paper_content stringlengths 7.54k 83.7k | prompt stringlengths 161 2.5k | format stringclasses 5
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ICLR | Title
Toward Efficient Low-Precision Training: Data Format Optimization and Hysteresis Quantization
Abstract
As the complexity and size of deep neural networks continue to increase, lowprecision training has been extensively studied in the last few years to reduce hardware overhead. Training performance is largely affe... | 1. What is the focus of the paper regarding efficient deep neural network training using quantized networks?
2. What are the strengths of the proposed approach, particularly the hysteresis quantization scheme?
3. Are there any concerns or weaknesses regarding the experiment settings and comparisons with other works?
4.... | Summary Of The Paper
Review | Summary Of The Paper
The submission starts from an interesting point that various quantized training environments may require different formats for training accurate deep neural networks. They present a metric based on the misalignment between
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to determine the optimal format. To mitigate... |
ICLR | Title
Certified Robustness on Structural Graph Matching
Abstract
The vulnerability of graph matching (GM) to adversarial attacks has received increasing attention from emerging empirical studies, while the certified robustness of GM has not been explored. Inspired by the technique of randomized smoothing, in this paper... | 1. What is the focus and contribution of the paper regarding probabilistic robustness in graph matching?
2. What are the strengths and weaknesses of the proposed approach for building a probabilistically robust classifier?
3. Do you have any concerns or questions about the motivation and problem setting of the paper?
4... | Summary Of The Paper
Strengths And Weaknesses
Clarity, Quality, Novelty And Reproducibility | Summary Of The Paper
The paper considers the problem of the probabilistic robustness of graph-matching (GM) algorithms against norm-based adversarial perturbations. The authors develop new algorithms for randomized smoothing for GM by exploiting the structure of the problem. They start by decomposing the robust matchin... |
ICLR | Title
Certified Robustness on Structural Graph Matching
Abstract
The vulnerability of graph matching (GM) to adversarial attacks has received increasing attention from emerging empirical studies, while the certified robustness of GM has not been explored. Inspired by the technique of randomized smoothing, in this paper... | 1. What is the focus and contribution of the paper on certified robustness for graph matching?
2. What are the strengths of the proposed approach, particularly in terms of its novelty and experimental results?
3. What are the weaknesses of the paper, especially regarding its potential applications and limitations in in... | Summary Of The Paper
Strengths And Weaknesses
Clarity, Quality, Novelty And Reproducibility | Summary Of The Paper
This paper provides a new result on certified robustness for graph matching. Its based on randomized smoothing (Cohen 2019), but the authors use a correlation matrix based on the graph information to construct a joint Gaussian distribution for smoothing (vs the standard single Gaussian distribution... |
ICLR | Title
Certified Robustness on Structural Graph Matching
Abstract
The vulnerability of graph matching (GM) to adversarial attacks has received increasing attention from emerging empirical studies, while the certified robustness of GM has not been explored. Inspired by the technique of randomized smoothing, in this paper... | 1. What is the focus and contribution of the paper regarding structural graph matching?
2. How does the proposed approach apply the generic randomized smoothing technique to graph matching?
3. What are the strengths and weaknesses of the paper, particularly in its formulation and application?
4. Do you have any questio... | Summary Of The Paper
Strengths And Weaknesses
Clarity, Quality, Novelty And Reproducibility | Summary Of The Paper
The paper studies the problem of certifying the robustness of structural graph matching, where the goal is to return a matching (or assignment) between the nodes of two input graphs G1 and G2, based on node and edge attributes. A robust graph matching mechanism is supposed to be resilient to advers... |
ICLR | Title
Certified Robustness on Structural Graph Matching
Abstract
The vulnerability of graph matching (GM) to adversarial attacks has received increasing attention from emerging empirical studies, while the certified robustness of GM has not been explored. Inspired by the technique of randomized smoothing, in this paper... | 1. What is the focus of the paper regarding graph matching?
2. What are the strengths of the proposed approach, particularly in terms of its theoretical analysis?
3. Do you have any concerns about the similarity of the technique used in the paper to other works?
4. How would you assess the clarity, quality, novelty, an... | Summary Of The Paper
Strengths And Weaknesses
Clarity, Quality, Novelty And Reproducibility | Summary Of The Paper
This paper considers the certified robustness of graph matching. Graph matching aims to find a matching between nodes of two graphs that maximizes the overall affinity score. This paper defines the certified robustness on graph matching by using randomized smoothing to the node classification stage... |
ICLR | Title
Self-Supervised Variational Auto-Encoders
Abstract
Density estimation, compression, and data generation are crucial tasks in artificial intelligence. Variational Auto-Encoders (VAEs) constitute a single framework to achieve these goals. Here, we present a novel class of generative models, called self-supervised V... | 1. What are the novel aspects introduced by the paper regarding VAEs?
2. What are the concerns regarding the transformation from x to y?
3. How does the reviewer assess the hierarchical self-supervised VAE and its potential impact on the inference process?
4. Are there any questions about the necessity of conditional i... | Review | Review
This paper targets richer and higher-quality generation with VAE. Two techniques are adopted to achieve the goal: 1). bijective model to enrich data generation with flexible prior. 2). presenting compressed variants of the input data, i.e. self -supervision as additional condition
y
, for reconstruction. The two... |
ICLR | Title
Self-Supervised Variational Auto-Encoders
Abstract
Density estimation, compression, and data generation are crucial tasks in artificial intelligence. Variational Auto-Encoders (VAEs) constitute a single framework to achieve these goals. Here, we present a novel class of generative models, called self-supervised V... | 1. What is the focus of the paper, particularly regarding the proposed self-supervised variational autoencoder?
2. What are the unique aspects of the method introduced in the paper, such as downscaling and edge detection?
3. How does the reviewer assess the clarity and quality of the paper's content, including its orga... | Review | Review
Summary
The paper presents a self-supervised variational auto-encoder called selfVAE. The work proposes the use of downscaling and edge detection as simpler representations of the input images to be reconstructed. The model should then learn to improve the low dimensional approximations to recover the higher dim... |
ICLR | Title
Self-Supervised Variational Auto-Encoders
Abstract
Density estimation, compression, and data generation are crucial tasks in artificial intelligence. Variational Auto-Encoders (VAEs) constitute a single framework to achieve these goals. Here, we present a novel class of generative models, called self-supervised V... | 1. What are the strengths and weaknesses of the proposed approach in addressing the problem of VAEs ignoring some dimensions of the latent code?
2. How does the addition of self-supervised tasks improve the latent representation, and what is the effect of their performance on the quality of the representations?
3. How ... | Review | Review
################################### Pros:
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VAEs can ignore some dimensions of the latent code. Enforcing the posterior distributions to consider desired factors of variations in the input can be fulfilled by either making it more structured (i.e., quantization as in VQ-VAE-2) or introducing additional constrain... |
ICLR | Title
Self-Supervised Variational Auto-Encoders
Abstract
Density estimation, compression, and data generation are crucial tasks in artificial intelligence. Variational Auto-Encoders (VAEs) constitute a single framework to achieve these goals. Here, we present a novel class of generative models, called self-supervised V... | 1. What is the main contribution of the paper, and how does it differ from previous works?
2. What are the strengths and weaknesses of the proposed method, particularly regarding its technical aspects and derivations?
3. How does the reviewer assess the novelty and effectiveness of the proposed approach compared to oth... | Review | Review
This paper focuses on the task of generating high-quality data with generative models. To be specific, the authors proposed a variant of variational autoencoder (VAE) model, named self-supervised VAE. The intuition behind this model is that by breaking down the complex generation task into simpler/smaller ones, ... |
ICLR | Title
PANDA - Adapting Pretrained Features for Anomaly Detection
Abstract
Anomaly detection methods require high-quality features. One way of obtaining strong features is to adapt pre-trained features to anomaly detection on the target distribution. Unfortunately, simple adaptation methods often result in catastrophic ... | 1. What is the focus of the paper regarding image anomaly detection?
2. What are the strengths of the proposed approach, particularly in addressing catastrophic collapse?
3. What are the weaknesses of the paper, especially regarding the novelty of the proposed solutions?
4. Do you have any concerns or suggestions regar... | Review | Review
Methodology: The paper studies the problem of pre-trained model adaptation for image anomaly detection. It is argued that previous model adaptation schemes either lost model capacity (DeepSVDD) or required extra data with marginal improvement (joint optimization). To alleviate these issues, the paper proposes to... |
ICLR | Title
PANDA - Adapting Pretrained Features for Anomaly Detection
Abstract
Anomaly detection methods require high-quality features. One way of obtaining strong features is to adapt pre-trained features to anomaly detection on the target distribution. Unfortunately, simple adaptation methods often result in catastrophic ... | 1. How does the paper define normal and anomaly in images?
2. Why does the paper argue that using pre-trained features in anomaly detection has the combating collapse?
3. Can the proposed method work for other computer vision tasks or very related tasks, such as video anomaly detection?
4. What is the technical contrib... | Review | Review
The paper lacks of a clear definition of normal and anomaly in images. For example, this could be illustrated in the intro or in each dataset separately. Qualitative results could also be helpful examples. Otherwise, it is hard to understand why each proposed method works, and only quantitative results are not e... |
ICLR | Title
PANDA - Adapting Pretrained Features for Anomaly Detection
Abstract
Anomaly detection methods require high-quality features. One way of obtaining strong features is to adapt pre-trained features to anomaly detection on the target distribution. Unfortunately, simple adaptation methods often result in catastrophic ... | 1. What is the focus of the paper, and what are the proposed contributions?
2. What are the strengths and weaknesses of the proposed approach, particularly in terms of its practical application?
3. Are there any concerns or questions regarding the effectiveness or novelty of the proposed method?
4. How does the reviewe... | Review | Review
This paper proposed an algorithm for anomaly detection. The core of the approach includes two parts. One is sample-wise early stopping and anther is a new type of loss. Although the experiment on the proposed algorithm has better performance on some datasets such as MNIST and CIFAR10, the proposed method does no... |
ICLR | Title
PANDA - Adapting Pretrained Features for Anomaly Detection
Abstract
Anomaly detection methods require high-quality features. One way of obtaining strong features is to adapt pre-trained features to anomaly detection on the target distribution. Unfortunately, simple adaptation methods often result in catastrophic ... | 1. What are the strengths and weaknesses of the paper regarding its contributions to combating feature collapse during model adaptation in anomaly detection?
2. How does the reviewer assess the effectiveness and efficiency of the two proposed approaches, adaptive sample-based early stopping (SES) and continual learning... | Review | Review
This paper proposes a method to combat feature collapse during model adaptation in anomaly detection (AD), while maintaining performance gains. Feature collapse happens during fine-tuning adaptation of a pretrained model when using a compactness loss and results in all samples, even the anomalous ones, being map... |
ICLR | Title
TaskSet: A Dataset of Optimization Tasks
Abstract
We present TaskSet, a dataset of tasks for use in training and evaluating optimizers. TaskSet is unique in its size and diversity, containing over a thousand tasks ranging from image classification with fully connected or convolutional neural networks, to variatio... | 1. What is the main contribution of the paper regarding the learned optimizers?
2. What are the strengths and weaknesses of the proposed dataset of tasks for evaluating learned optimizers?
3. Do you have any concerns about the choices made in creating the dataset and how they impact future research?
4. How does the rev... | Review | Review
Summary:
This paper proposes a dataset of tasks to help evaluate learned optimizers. The learned optimizers are evaluated by the loss that they achieve on held-out tasks after 10k steps. Using this dataset, the main strategy considered is to use search spaces that parametrize optimizers and learn a list of hyper... |
ICLR | Title
TaskSet: A Dataset of Optimization Tasks
Abstract
We present TaskSet, a dataset of tasks for use in training and evaluating optimizers. TaskSet is unique in its size and diversity, containing over a thousand tasks ranging from image classification with fully connected or convolutional neural networks, to variatio... | 1. What is the focus of the paper, and what are the authors trying to achieve?
2. What are the strengths and weaknesses of the proposed approach, particularly regarding its practical applications?
3. Do you have any concerns or questions about the experimental design and results presented in the paper?
4. How does the ... | Review | Review
This paper proposes a new dataset which contains experiment / model details coupled with optimizer information so as to model the behavior of optimizer, and their effect on performance on test set. The paper is not very difficult to follow, but I am not super convinced of an actual practical use cases.
I think t... |
ICLR | Title
TaskSet: A Dataset of Optimization Tasks
Abstract
We present TaskSet, a dataset of tasks for use in training and evaluating optimizers. TaskSet is unique in its size and diversity, containing over a thousand tasks ranging from image classification with fully connected or convolutional neural networks, to variatio... | 1. What is the main contribution of the paper regarding learned optimizers?
2. What are the strengths and weaknesses of the proposed TaskSet?
3. How does the reviewer assess the presentation and clarity of the paper's content?
4. What are the minor comments and concerns regarding Figure 1b and the application of TaskSe... | Review | Review
The paper presents a suite of deep learning focused optimization problems that would facilitate the development of learned optimizers. This is very useful and can streamline research in learned optimizers while providing a benchmarking suite that can be used for training as well as evaluation. In my opinion, thi... |
ICLR | Title
TaskSet: A Dataset of Optimization Tasks
Abstract
We present TaskSet, a dataset of tasks for use in training and evaluating optimizers. TaskSet is unique in its size and diversity, containing over a thousand tasks ranging from image classification with fully connected or convolutional neural networks, to variatio... | 1. What is the purpose and significance of TaskSet in optimizing learning tasks?
2. How effective is TaskSet in evaluating and choosing optimizers for various tasks?
3. Are there any concerns regarding overfitting to specific tasks when utilizing TaskSet?
4. Can we assume that an optimizer chosen using TaskSet will per... | Review | Review
This work presents TaskSet, a collection of optimization tasks consisting of different combinations of data, loss function, and network architecture. The tasks are useful when choosing and evaluating different optimizers (e.g. ADAM) for learning tasks. The usefulness of this collection is demonstrated for a hype... |
ICLR | Title
Visually-Augmented Language Modeling
Abstract
Human language is grounded on multimodal knowledge including visual knowledge like colors, sizes, and shapes. However, current large-scale pre-trained language models rely on text-only self-supervised training with massive text data, which precludes them from utilizin... | 1. What is the focus and contribution of the paper regarding image-text data?
2. What are the strengths of the proposed approach, particularly in its novel components and empirical results?
3. What are the weaknesses of the paper, especially concerning the image retrieval component?
4. Do you have any concerns or sugge... | Summary Of The Paper
Strengths And Weaknesses
Clarity, Quality, Novelty And Reproducibility | Summary Of The Paper
This paper proposes a pre-training framework, called VALM, to jointly train on image-text data. The novelty of this work, compared to previous works in similar field, is how the image-text pairs are created. While previous works use pre-curated image-text aligned pairs, this work instead uses image... |
ICLR | Title
Visually-Augmented Language Modeling
Abstract
Human language is grounded on multimodal knowledge including visual knowledge like colors, sizes, and shapes. However, current large-scale pre-trained language models rely on text-only self-supervised training with massive text data, which precludes them from utilizin... | 1. What is the main contribution of the paper regarding language models and visual knowledge?
2. What are the strengths and weaknesses of the proposed approach, particularly in its ability to incorporate external visual knowledge and its limitations in evaluating retrieved images and impact on runtime?
3. How does the ... | Summary Of The Paper
Strengths And Weaknesses
Clarity, Quality, Novelty And Reproducibility | Summary Of The Paper
This paper improves a language model's performance on pure language tasks about visual concepts by augmenting its internal representation with dynamically retrieved images.
(motivation) Global context (external knowledge about entities, relations, etc.) has been incorporated into pre-trained langua... |
ICLR | Title
Visually-Augmented Language Modeling
Abstract
Human language is grounded on multimodal knowledge including visual knowledge like colors, sizes, and shapes. However, current large-scale pre-trained language models rely on text-only self-supervised training with massive text data, which precludes them from utilizin... | 1. What is the main contribution of the paper, and how does it address the problem of injecting visual information into language models?
2. What are the strengths and weaknesses of the proposed method, particularly regarding its novelty and computational efficiency?
3. Are there any concerns or questions regarding the ... | Summary Of The Paper
Strengths And Weaknesses
Clarity, Quality, Novelty And Reproducibility | Summary Of The Paper
To augment language models with relevant visual information, a visually-augmented language model (VALM) is proposed in this paper. The core idea is to retrieve relevant images by CLIP model and then fuse them to the second last Transformer layer of PLM.
Strengths And Weaknesses
Strengths:
The rese... |
ICLR | Title
Visually-Augmented Language Modeling
Abstract
Human language is grounded on multimodal knowledge including visual knowledge like colors, sizes, and shapes. However, current large-scale pre-trained language models rely on text-only self-supervised training with massive text data, which precludes them from utilizin... | 1. What is the main contribution of the paper regarding visual-augmented language modeling?
2. What are the strengths and weaknesses of the proposed architecture and evaluation methodology?
3. How does the reviewer assess the clarity, quality, novelty, and reproducibility of the paper's content?
4. Are there any concer... | Summary Of The Paper
Strengths And Weaknesses
Clarity, Quality, Novelty And Reproducibility | Summary Of The Paper
This work proposes a novel architecture to do visual-augmented language modeling. Before each prediction of the next word, this architecture queries the most relevant images w.r.t. the current received part of the sentence using a pretrained CLIP model and a large image database. The authors traine... |
ICLR | Title
Partitioned Learned Bloom Filters
Abstract
Bloom filters are space-efficient probabilistic data structures that are used to test whether an element is a member of a set, and may return false positives. Recently, variations referred to as learned Bloom filters were developed that can provide improved performance i... | 1. What is the main contribution of the paper in the field of semantic correspondence?
2. What are the strengths of the proposed approach, particularly in terms of neural representation?
3. Do you have any concerns regarding the semantic correspondence representation?
4. What are the limitations of the NeMF approach?
5... | Review | Review
A clear exposition of the problem and proposed solution, the paper key strength is in the formulation of the partitioned bloom filter as an optimization problem that generalizes previously proposed architectures, and prescribes an interpretable solution for the choice of the optimal partition-thresholds in terms... |
ICLR | Title
Partitioned Learned Bloom Filters
Abstract
Bloom filters are space-efficient probabilistic data structures that are used to test whether an element is a member of a set, and may return false positives. Recently, variations referred to as learned Bloom filters were developed that can provide improved performance i... | 1. What is the focus of the paper regarding the sandwiched Bloom filter model?
2. What are the strengths of the proposed approach, particularly in terms of its ability to maintain multiple score partitions?
3. What are the weaknesses of the paper, especially regarding its clarity and experimental results?
4. Do you hav... | Review | Review
The paper proposes a generalization of the sandwiched Bloom filter model that maintais a set of score partitions instead of just two and an algorithm for optimizing parameters of the partition under the target false-positive rate. Authors evaluate partitioned Bloom filter on three datasets and demonstrate that d... |
ICLR | Title
Partitioned Learned Bloom Filters
Abstract
Bloom filters are space-efficient probabilistic data structures that are used to test whether an element is a member of a set, and may return false positives. Recently, variations referred to as learned Bloom filters were developed that can provide improved performance i... | 1. What is the main contribution of the paper regarding the fine-tuning of partitioned learned Bloom filters?
2. What are the strengths of the proposed approach, particularly in terms of its ability to reduce space consumption and false positive rates?
3. What are the weaknesses or limitations of the proposed method, e... | Review | Review
This work proposed a technique to fine tune the partitioned learned Bloom filter to reduce the space consumption given a false positive rate threshold.
The idea is to formulate the problem into a two-part optimization problem: How to best partition the scores from the model into a given number of regions and how... |
ICLR | Title
SEARNN: Training RNNs with global-local losses
Abstract
We propose SEARNN, a novel training algorithm for recurrent neural networks (RNNs) inspired by the “learning to search” (L2S) approach to structured prediction. RNNs have been widely successful in structured prediction applications such as machine translatio... | 1. What is the main contribution of the paper regarding RNNs?
2. What are the strengths of the proposed approach, particularly in resolving issues with local ground truth choices?
3. How does the reviewer assess the efficacy of the technique based on the experiments conducted?
4. What is the significance of scaling SEA... | Review | Review
This paper extends the concept of global rather than local optimization from the learning to search (L2S) literature to RNNs, specifically in the formation and implementation of SEARNN. Their work takes steps to consider and resolve issues that arise from restricting optimization to only local ground truth choic... |
ICLR | Title
SEARNN: Training RNNs with global-local losses
Abstract
We propose SEARNN, a novel training algorithm for recurrent neural networks (RNNs) inspired by the “learning to search” (L2S) approach to structured prediction. RNNs have been widely successful in structured prediction applications such as machine translatio... | 1. What is the focus of the paper, and what are the proposed contributions?
2. What are the strengths of the paper, particularly in its literature review and adaptation of SEARN to RNNs?
3. What are the weaknesses of the paper, such as the lack of direct comparisons and experimental details?
4. Do you have any concerns... | Review | Review
This paper proposes an adaptation of the SEARN algorithm to RNNs for generating text. In order to do so, they discuss various issues on how to scale the approach to large output vocabularies by sampling which actions the algorithm to explore.
Pros:
- Good literature review. But the future work on bandits is alr... |
ICLR | Title
SEARNN: Training RNNs with global-local losses
Abstract
We propose SEARNN, a novel training algorithm for recurrent neural networks (RNNs) inspired by the “learning to search” (L2S) approach to structured prediction. RNNs have been widely successful in structured prediction applications such as machine translatio... | 1. What is the main contribution of the paper in the field of RNN training?
2. How does the proposed method, SeaRnn, overcome the limitations of local optimization in RNN training?
3. Can you explain how SeaRnn improves the results obtained by MLE training in three different problems?
4. How does the paper demonstrate ... | Review | Review
The paper proposes new RNN training method based on the SEARN learning to search (L2S) algorithm and named as SeaRnn. It proposes a way of overcoming the limitation of local optimization trough the exploitation of the structured losses by L2S. It can consider different classifiers and loss functions, and a sampl... |
ICLR | Title
Neuro-Symbolic Ontology-Mediated Query Answering
Abstract
Recently, low-dimensional vector space representations of Knowledge Graphs (KGs) have been applied to find answers to logical queries over incomplete KGs. However, the current methods only focus on inductive reasoning, i.e. answering such queries by predic... | 1. What is the focus of the paper regarding formal query answering over an incomplete knowledge graph?
2. What are the strengths of the paper, particularly in providing new datasets and proposing extensions to enhance existing methods?
3. What are the weaknesses of the paper, especially regarding the design of the exte... | Summary Of The Paper
Review | Summary Of The Paper
The paper targets a problem about formal query answering over an incomplete knowledge graph with a background DL-Lite_R ontology, where the formal queries are restricted to certain shapes. The paper extends existing methods for embedding query answering over knowledge graph without background ontol... |
ICLR | Title
Neuro-Symbolic Ontology-Mediated Query Answering
Abstract
Recently, low-dimensional vector space representations of Knowledge Graphs (KGs) have been applied to find answers to logical queries over incomplete KGs. However, the current methods only focus on inductive reasoning, i.e. answering such queries by predic... | 1. What is the main contribution of the paper in the field of query answering systems?
2. What are the strengths of the proposed approach, particularly in its ability to integrate inductive and deductive reasoning capabilities?
3. What are the weaknesses of the paper, especially regarding the experimental results of th... | Summary Of The Paper
Review | Summary Of The Paper
This paper proposes ontology mediated Neuro symbolic query answering system that uses popular embeddings and knowledge present in the ontology to bring the inductive and deductive reasoning capabilities for query answering.
Paper proposes strategies to use ontology axioms to improve embedding train... |
ICLR | Title
Neuro-Symbolic Ontology-Mediated Query Answering
Abstract
Recently, low-dimensional vector space representations of Knowledge Graphs (KGs) have been applied to find answers to logical queries over incomplete KGs. However, the current methods only focus on inductive reasoning, i.e. answering such queries by predic... | 1. What is the main contribution of the paper regarding ontology-mediated query answering?
2. What are the strengths and weaknesses of the proposed approach compared to prior works?
3. Do you have any concerns or questions regarding the experimental setup and results?
4. How does the reviewer assess the novelty and lim... | Summary Of The Paper
Review | Summary Of The Paper
Authors study the problem of ontology-mediated query answering (OMQA) over knowledge graphs (KGs). The problem definition is as follows: given an ontology, an incomplete KG, and a monadic (positive) conjunctive query (CQ) which does not have a match in the given KG, predict/rank/score the most like... |
ICLR | Title
A Closer Look at Few-shot Classification
Abstract
Few-shot classification aims to learn a classifier to recognize unseen classes during training with limited labeled examples. While significant progress has been made, the growing complexity of network designs, meta-learning algorithms, and differences in implemen... | 1. How can we evaluate meta-learning algorithms in a consistent and systematic way?
2. Are there any simple modifications that can improve the baselines of existing meta-learning algorithms?
3. Can we get insights into why certain meta-learning algorithms may not be as effective as claimed, and what can be done to impr... | Review | Review
The paper tried to propose a systematic/consistent way for evaluating meta-learning algorithms. I believe this is a great direction of research as the meta-learning community is growing quickly. However, my question is if a relatively simple modification could improve the baselines, are there simple modification... |
ICLR | Title
A Closer Look at Few-shot Classification
Abstract
Few-shot classification aims to learn a classifier to recognize unseen classes during training with limited labeled examples. While significant progress has been made, the growing complexity of network designs, meta-learning algorithms, and differences in implemen... | 1. What are the strengths and weaknesses of the paper's experimental design?
2. How does the reviewer assess the validity and practicality of the paper's comparison of few-shot learning methods?
3. What are the limitations of the paper regarding the number of novel classes in the training and testing stages?
4. How doe... | Review | Review
This paper gives a nice overview of existing works on few-shot learning. It groups them into some intuitive categories and meanwhile distills a common framework (Figure 2) employed by the methods. Moreover, the authors selected four of them, along with two baselines, to experimentally compare their performances ... |
ICLR | Title
A Closer Look at Few-shot Classification
Abstract
Few-shot classification aims to learn a classifier to recognize unseen classes during training with limited labeled examples. While significant progress has been made, the growing complexity of network designs, meta-learning algorithms, and differences in implemen... | 1. What are the strengths and weaknesses of the paper's approach to evaluating few-shot learning methods?
2. How does the reviewer feel about the inclusion of domain shift experiments in the paper?
3. Are there any concerns regarding the redundancy of information in the paper, specifically in Sections 2 and 3.3, and Ta... | Review | Review
There are a few things I like about the paper.
Firstly, it makes interesting observations about the evaluation of the few-shot learning approaches, e.g. the underestimated baselines, and compares multiple methods in the same conditions. In fact, one of the reasons for accepting this paper would be to get a uni... |
ICLR | Title
Non-asymptotic Confidence Intervals of Off-policy Evaluation: Primal and Dual Bounds
Abstract
Off-policy evaluation (OPE) is the task of estimating the expected reward of a given policy based on offline data previously collected under different policies. Therefore, OPE is a key step in applying reinforcement lear... | 1. What is the main contribution of the paper regarding off-policy evaluation?
2. What are the strengths of the paper, particularly in terms of its improvement over prior works?
3. Do you have any concerns or questions about the method's performance when the functions do not lie in an RKHS?
4. How does the reviewer ass... | Review | Review
This work constructs non-asymptotic confidence intervals for off-policy evaluation. This is achieved by assuming that the reward at any given time only depends on the state action pair, leveraging that assumed structure to define the difference between the empirical and estimated bellman residual operators as a ... |
ICLR | Title
Non-asymptotic Confidence Intervals of Off-policy Evaluation: Primal and Dual Bounds
Abstract
Off-policy evaluation (OPE) is the task of estimating the expected reward of a given policy based on offline data previously collected under different policies. Therefore, OPE is a key step in applying reinforcement lear... | 1. What is the focus of the paper regarding off-policy evaluation?
2. What are the strengths of the proposed method, particularly in its ability to improve confidence intervals?
3. What are the weaknesses of the paper, especially regarding its claims and experiments?
4. Do you have any concerns about the method's appli... | Review | Review
The objective of this paper is to provide a method to produce tighter confidence intervals for off-policy evaluation. The paper claims to develop a new primal-dual perspective on OPE confidence intervals and a tight concentration inequality. It develops both theoretical and empirical evidence to support its clai... |
ICLR | Title
Non-asymptotic Confidence Intervals of Off-policy Evaluation: Primal and Dual Bounds
Abstract
Off-policy evaluation (OPE) is the task of estimating the expected reward of a given policy based on offline data previously collected under different policies. Therefore, OPE is a key step in applying reinforcement lear... | 1. What is the focus of the paper regarding Markov decision processes?
2. What are the strengths and weaknesses of the proposed optimization-based method for off-policy evaluation?
3. How does the reviewer assess the significance and novelty of the work compared to prior methods?
4. Are there any concerns regarding the... | Review | Review
General overview The paper studies an off-policy evaluation (OPE) problem for Markov decision processes (MDPs). It suggests an optimization-based method that can construct a non-asymptotic confidence interval, for a given confidence level, for the value function of a policy starting from a fixed initial distribu... |
ICLR | Title
Non-asymptotic Confidence Intervals of Off-policy Evaluation: Primal and Dual Bounds
Abstract
Off-policy evaluation (OPE) is the task of estimating the expected reward of a given policy based on offline data previously collected under different policies. Therefore, OPE is a key step in applying reinforcement lear... | 1. How does the proposed approach improve upon previous methods in terms of sample efficiency and optimization?
2. Can the method provide point estimates of policy values, and if so, how would they be derived?
3. What are the key insights or findings from the ablation study in Appendix H that could be highlighted in th... | Review | Review
This paper proposes an approach to construct confidence intervals using finite samples for off-policy evaluation. The paper improves the bound of a previous paper from
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and avoids solving global optimum by introducing the dual. It is also noted that the results do not only apply... |
ICLR | Title
SHAMANN: Shared Memory Augmented Neural Networks
Abstract
Current state-of-the-art methods for semantic segmentation use deep neural networks to learn the segmentation mask from the input image signal as an imageto-image mapping. While these methods effectively exploit global image context, the learning and compu... | 1. What is the focus of the paper regarding semantic segmentation?
2. What are the strengths and weaknesses of the proposed approach in comparison to prior works?
3. How does the reviewer assess the handling of long-range dependencies in the paper's approach?
4. What are the limitations of the experimental analysis, pa... | Review | Review
The authors applied the external memory module proposed by Graves et al. (2016) to the image segmentation task. SHAMANN is an extension to allow memory sharing between directions.
Authors claimed that one of the contributions is a reformulation of the semantic segmentation problem as a sequence learning task.
... |
ICLR | Title
SHAMANN: Shared Memory Augmented Neural Networks
Abstract
Current state-of-the-art methods for semantic segmentation use deep neural networks to learn the segmentation mask from the input image signal as an imageto-image mapping. While these methods effectively exploit global image context, the learning and compu... | 1. What is the main contribution of the paper in the field of semantic segmentation?
2. What are the strengths and weaknesses of the proposed method, particularly regarding its novelty and potential impact?
3. Do you have any questions or concerns regarding the processing order of patches and its potential impact on th... | Review | Review
The authors present a model for semantic segmentation. The proposed method casts the full image segmentation as a sequence of local segmentation predictions. The image is split in multiple patches and processed sequentially in some order. A shared memory allows the local patch predictions to propagate informatio... |
ICLR | Title
SHAMANN: Shared Memory Augmented Neural Networks
Abstract
Current state-of-the-art methods for semantic segmentation use deep neural networks to learn the segmentation mask from the input image signal as an imageto-image mapping. While these methods effectively exploit global image context, the learning and compu... | 1. What is the focus of the paper regarding semantic segmentation?
2. What are the strengths and weaknesses of the proposed approach compared to prior works?
3. Do you have any concerns or suggestions regarding the experimental results and comparisons?
4. What are some specific questions regarding the methodology and t... | Review | Review
Summary:
The paper proposes a system of semantic segmentation based on sequential processing of the image in a patch-wise manner with multiple "actors", sharing a common external memory. This approach stands in contrast to the more usual approach of single-shot prediction for the whole image, where encoder-decod... |
ICLR | Title
CNN Compression and Search Using Set Transformations with Width Modifiers on Network Architectures
Abstract
We propose a new approach, based on discrete filter pruning, to adapt off-the-shelf models into an embedded environment. Importantly, we circumvent the usually prohibitive costs of model compression. Our me... | 1. What is the focus and contribution of the paper on compressing convolutional networks?
2. What are the strengths and weaknesses of the proposed SCBP framework, particularly regarding its effectiveness and novelty?
3. Do you have any concerns or suggestions regarding the segmentation and c-ratio set in the method?
4.... | Summary Of The Paper
Strengths And Weaknesses
Clarity, Quality, Novelty And Reproducibility | Summary Of The Paper
This paper demonstrates SCBP, a framework to compress convolutional networks, by first segmenting the layers into superblocks and then associate them with different compression ratios. The SCBP framework constructs a pool of models with different attributes that meet different needs.
Strengths And... |
ICLR | Title
CNN Compression and Search Using Set Transformations with Width Modifiers on Network Architectures
Abstract
We propose a new approach, based on discrete filter pruning, to adapt off-the-shelf models into an embedded environment. Importantly, we circumvent the usually prohibitive costs of model compression. Our me... | 1. What is the focus of the paper regarding convolutional neural network architecture?
2. What are the strengths and weaknesses of the proposed approach in comparison to other methods?
3. How does the reviewer assess the clarity, quality, novelty, and reproducibility of the paper's content? | Summary Of The Paper
Strengths And Weaknesses
Clarity, Quality, Novelty And Reproducibility | Summary Of The Paper
The authors propose a systematic method of creating variations of a convolutional architecture by reducing the number of filters at certain groups of layers. Subsequently training and evaluating these new architectures shows that some are significantly more efficient that the initial "seed" archite... |
ICLR | Title
CNN Compression and Search Using Set Transformations with Width Modifiers on Network Architectures
Abstract
We propose a new approach, based on discrete filter pruning, to adapt off-the-shelf models into an embedded environment. Importantly, we circumvent the usually prohibitive costs of model compression. Our me... | 1. What is the focus of the paper regarding efficient neural network design?
2. What are the strengths of the proposed approach, particularly in tackling the problem of designing efficient neural architectures?
3. What are the weaknesses of the paper, especially regarding the grid search method and lack of proper justi... | Summary Of The Paper
Strengths And Weaknesses
Clarity, Quality, Novelty And Reproducibility | Summary Of The Paper
This paper studies efficient neural network design for faster inference. The authors propose Structured Coarse Block Pruning (SCBP) that first partitions all layers into segments, then assigns a width multiplier to each stage, and finally explores the best configuration by grid search. The proposed... |
ICLR | Title
Cross-Corpus Training with TreeLSTM for the Extraction of Biomedical Relationships from Text
Abstract
A bottleneck problem in machine learning-based relationship extraction (RE) algorithms, and particularly of deep learning-based ones, is the availability of training data in the form of annotated corpora. For spe... | 1. What is the main contribution of the paper in relation extraction?
2. What are the strengths and weaknesses of the proposed cross-corpus approach?
3. How does the reviewer assess the clarity and quality of the paper's content?
4. What are the concerns regarding the experimental setup and results?
5. Do you have any ... | Review | Review
SUMMARY.
The paper presents a cross-corpus approach for relation extraction from text.
The main idea is complementing small training data for relation extraction with training data with different relation types.
The model is also connected with multitask learning approaches where the encoder for the input is th... |
ICLR | Title
Cross-Corpus Training with TreeLSTM for the Extraction of Biomedical Relationships from Text
Abstract
A bottleneck problem in machine learning-based relationship extraction (RE) algorithms, and particularly of deep learning-based ones, is the availability of training data in the form of annotated corpora. For spe... | 1. What are the strengths and weaknesses of the paper's experimental results?
2. Are there any concerns or suggestions regarding the presentation of the research findings?
3. Is there any question about the methodology used in the study, such as the use of entity type information?
4. Are there any inconsistencies in th... | Review | Review
This is a well-written paper with sound experiments. However, the research outcome is not very surprising.
- Only macro-average F-scores are reported. Please present micro-average scores as well.
- The detailed procedure of relation extraction should be described. How do you use entity type information? (Proba... |
ICLR | Title
Cross-Corpus Training with TreeLSTM for the Extraction of Biomedical Relationships from Text
Abstract
A bottleneck problem in machine learning-based relationship extraction (RE) algorithms, and particularly of deep learning-based ones, is the availability of training data in the form of annotated corpora. For spe... | 1. What are the major issues with the paper's writing and formatting?
2. How does the proposed method handle instances with more than two entities?
3. What makes the paper's approach novel, despite using existing deep learning models?
4. Are there any relevant references or citations missing from the paper? | Review | Review
This paper proposes to use Cross-Corpus training for biomedical relationship extraction from text.
- Many wording issues, like citation formats, grammar mistakes, missing words,
e.g., Page 2: it as been
- The description of the methods should be improved.
For instance, why the input has only two entit... |
ICLR | Title
Linear algebra with transformers
Abstract
Most applications of transformers to mathematics, from integration to theorem proving, focus on symbolic computation. In this paper, we show that transformers can be trained to perform numerical calculations with high accuracy. We consider problems of linear algebra: matr... | 1. What is the focus of the paper "Linear Algebra with Transformers"?
2. What are the strengths of the paper regarding its novel approach?
3. What are the weaknesses of the paper, particularly in terms of experimentation and limitations?
4. How can the findings of the paper be generalized to different distributions and... | Summary Of The Paper
Review | Summary Of The Paper
This paper “Linear algebra with transformers” studies the application of seq2seq transformers to matrix operations. It studies their performance across different encodings of floating point numbers, different sizes of matrices, different operations, and different (synthetic) data distributions. The... |
ICLR | Title
Linear algebra with transformers
Abstract
Most applications of transformers to mathematics, from integration to theorem proving, focus on symbolic computation. In this paper, we show that transformers can be trained to perform numerical calculations with high accuracy. We consider problems of linear algebra: matr... | 1. What is the focus of the paper regarding algebraic computations?
2. What are the strengths and weaknesses of the proposed approach in addressing the problem?
3. Do you have any concerns about the experimental design and its limitations?
4. How does the reviewer assess the impact and novelty of the paper's content?
5... | Summary Of The Paper
Review | Summary Of The Paper
This paper consider the problem of approximating algebraic computations over matrices using transformers.
Experiments with different encodings are presented, investigating the use of transformers for approximating a number of algebraic operations.
Review
While I found the paper well written, I did... |
ICLR | Title
Linear algebra with transformers
Abstract
Most applications of transformers to mathematics, from integration to theorem proving, focus on symbolic computation. In this paper, we show that transformers can be trained to perform numerical calculations with high accuracy. We consider problems of linear algebra: matr... | 1. What is the focus of the paper regarding linear algebra computations?
2. What are the strengths and weaknesses of the transformer model in performing various linear algebra tasks?
3. How does the reviewer assess the paper's framing and claims regarding real-world problems?
4. What additional data or analyses would t... | Summary Of The Paper
Review | Summary Of The Paper
The authors train generic, dense transformers to perform several standard linear algebra computations, ranging from simple tasks like transposition to complex nonlinear tasks such as matrix inversion. They restrict themselves to relatively small matrices due to the practical limits of the dense, qu... |
ICLR | Title
Linear algebra with transformers
Abstract
Most applications of transformers to mathematics, from integration to theorem proving, focus on symbolic computation. In this paper, we show that transformers can be trained to perform numerical calculations with high accuracy. We consider problems of linear algebra: matr... | 1. What is the focus of the paper, and what are the contributions of the proposed approach?
2. What are the strengths and weaknesses of the paper regarding its experiments, results, and conclusions?
3. Do you have any concerns about the significance or applicability of the paper's findings?
4. Are there any suggestions... | Summary Of The Paper
Review | Summary Of The Paper
This paper describes several experiments where transformers are trained to perform real-valued linear algebra calculations (matrix transposition, addition, multiplication, eigenvalues & eigenvectors of symmetric matrices, SVD, inversion). In-distribution accuracy is generally very high, whereas car... |
ICLR | Title
Improving Model Robustness with Latent Distribution Locally and Globally
Abstract
We propose a novel adversarial training method which leverages both the local and global information to defend adversarial attacks. Existing adversarial training methods usually generate adversarial perturbations locally in a superv... | 1. How does the paper analyze the property of local and global data manifolds in adversarial training?
2. What are the strengths of the proposed method, particularly in terms of its intuition and theoretical background?
3. What are the weaknesses of the paper, especially regarding the realization of equations 4 and 5 a... | Review | Review
The paper analyzes the property of local and global data manifold for adversarial training. In particular, they used a discriminator-classifier model, where the discriminator tries to differentiate between the natural and adversarial space, and the classifier aims to classify between them while maintaining the c... |
ICLR | Title
Improving Model Robustness with Latent Distribution Locally and Globally
Abstract
We propose a novel adversarial training method which leverages both the local and global information to defend adversarial attacks. Existing adversarial training methods usually generate adversarial perturbations locally in a superv... | 1. What is the focus of the paper regarding adversarial attacks and training?
2. What are the strengths of the proposed approach in combining different ideas?
3. What are the weaknesses of the paper regarding its evaluations and comparisons with other works?
4. Do you have any concerns about the significance of the nov... | Review | Review
Summary: This paper considers the local and global information in adversarial attacks for adversarial training, where the authors design an adversarial framework containing a discriminator and a classifier. The idea is interesting and the paper is easy to follow.
However, I have still some concerns below:
The no... |
ICLR | Title
Improving Model Robustness with Latent Distribution Locally and Globally
Abstract
We propose a novel adversarial training method which leverages both the local and global information to defend adversarial attacks. Existing adversarial training methods usually generate adversarial perturbations locally in a superv... | 1. What is the novel approach proposed by the paper in improving model robustness?
2. How does the method differ from existing literature in terms of regularization?
3. Are there any concerns regarding the experimental results or comparisons with baselines?
4. Is there any ambiguity in certain sentences or sections tha... | Review | Review
The paper proposes a new method of improving model robustness by generating adversarial samples that are regularized by their latent distribution through f-divergence, whereas existing literature only uses local manifold property such as smoothness.
The method is well-motivated and the clarity of the paper is go... |
ICLR | Title
Improving Model Robustness with Latent Distribution Locally and Globally
Abstract
We propose a novel adversarial training method which leverages both the local and global information to defend adversarial attacks. Existing adversarial training methods usually generate adversarial perturbations locally in a superv... | 1. What is the main contribution of the paper regarding adversarial robustness?
2. What are the strengths and weaknesses of the proposed framework for incorporating local and global structure of the data manifold?
3. Do you have any questions or concerns about the objective presented in Equations 4 and 5?
4. How does t... | Review | Review
This paper presents a framework for adversarial robustness via incorporating local and global structure of the data manifold. Specifically, the key motivation is that standard adversarial methods typically use only sample specific perturbations for generating the adversarial examples, and thus using them for rob... |
ICLR | Title
Representational correlates of hierarchical phrase structure in deep language models
Abstract
While contextual representations from pretrained Transformer models have set a new standard for many NLP tasks, there is not yet a complete accounting of their inner workings. In particular, it is not entirely clear what... | 1. What is the main contribution of the paper regarding linguistic structure and transformer models?
2. What are the strengths and weaknesses of the proposed distortion metric?
3. How does the reviewer assess the effectiveness and novelty of the experimental approach?
4. Do you have any suggestions for improving the pa... | Review | Review
The paper investigates the extent to which pre-trained transformer models successfully capture linguistic structure. The approach taken is to present the model with carefully constructed pairs of linguistic probes and then measure the difference in response to a naturally occurring sentence versus one the has be... |
ICLR | Title
Representational correlates of hierarchical phrase structure in deep language models
Abstract
While contextual representations from pretrained Transformer models have set a new standard for many NLP tasks, there is not yet a complete accounting of their inner workings. In particular, it is not entirely clear what... | 1. What is the focus of the review, particularly regarding the research question and methodology?
2. What are the strengths and weaknesses of the proposed approach, especially concerning the measurement used for analyzing representation sensitivity?
3. Are there any concerns or questions regarding the results and their... | Review | Review
The paper investigates the sensitivity of BERT representations to different kinds of permutations in the input sentence. These transformations include n-gram permutation, span swaps (with or without crossing syntactic phrase boundaries), adjacent token swaps (with different syntactic distance). The authors measu... |
ICLR | Title
Representational correlates of hierarchical phrase structure in deep language models
Abstract
While contextual representations from pretrained Transformer models have set a new standard for many NLP tasks, there is not yet a complete accounting of their inner workings. In particular, it is not entirely clear what... | 1. What is the focus of the paper, and what does it aim to achieve?
2. What is the methodology used in the paper, and how effective is it?
3. What are the results of the paper, and how significant are they?
4. Are there any limitations to the study, and how might they affect the conclusions drawn from it?
5. How does t... | Review | Review
This paper analyzes the ability of BERT model to learn good representations of sentences, through a purely empirical study, there is no other contribution of a new model or a new analysis technique. In the experimental analysis, phrases or words are swapped and the corresponding changes in the sentence represent... |
ICLR | Title
Representational correlates of hierarchical phrase structure in deep language models
Abstract
While contextual representations from pretrained Transformer models have set a new standard for many NLP tasks, there is not yet a complete accounting of their inner workings. In particular, it is not entirely clear what... | 1. What is the focus of the paper, and what are the novel findings regarding how pre-trained Transformer models build their contextual representations?
2. What are the strengths of the paper, particularly in its approach to studying the behavior of Transformer models?
3. What are the weaknesses of the paper, such as di... | Review | Review
Summary:
This paper addresses how pre-trained Transformer models build their contextual representations along with the layers by measuring changes in the outputs on a series of probes. Specifically, a probe involves swapping words in a sentence and measuring the distortion in the representations. The series of p... |
ICLR | Title
Representational correlates of hierarchical phrase structure in deep language models
Abstract
While contextual representations from pretrained Transformer models have set a new standard for many NLP tasks, there is not yet a complete accounting of their inner workings. In particular, it is not entirely clear what... | 1. What are the main contributions and strengths of the paper regarding BERT's output and parser trees?
2. What are the weaknesses or limitations of the paper, particularly in terms of theoretical explanations and novelty?
3. Do you have any concerns or suggestions regarding the experiments, such as deduced randomness ... | Review | Review
--- Overall ---
This paper provides some insights into the relation of BERT's output w.r.t. the parser tree (in terms of constituent phrases) of the input sentence. As some previous work has pointed out, BERT model contains the parsing information (e.g., Hewitt & Manning NAACL’19)). This work can be regarded as ... |
ICLR | Title
MGMA: Mesh Graph Masked Autoencoders for Self-supervised Learning on 3D Shape
Abstract
We introduce a self-supervised learning model to extract face nodes and global graph embeddings on meshes. We define a model with graph masking on a mesh graph composed of faces to pre-train on self-supervised tasks. We evaluat... | 1. What is the focus of the paper regarding mesh encoding?
2. What are the strengths and weaknesses of the proposed attention-based encoder architecture?
3. Do you have any concerns or questions about the contribution of the paper, particularly in comparison to other works like Point Transformer?
4. How would you asses... | Summary Of The Paper
Strengths And Weaknesses
Clarity, Quality, Novelty And Reproducibility | Summary Of The Paper
The paper proposes a novel attention based encoder architecture for meshes, where each node corresponds to a face on the mesh, the attention module aggregates information from the nodes, then the face node encodings are pooled into a global feature.
The paper also proposes a self-supervised task to... |
ICLR | Title
MGMA: Mesh Graph Masked Autoencoders for Self-supervised Learning on 3D Shape
Abstract
We introduce a self-supervised learning model to extract face nodes and global graph embeddings on meshes. We define a model with graph masking on a mesh graph composed of faces to pre-train on self-supervised tasks. We evaluat... | 1. What is the main contribution of the paper on mesh autoencoders?
2. What are the strengths and weaknesses of the proposed approach, particularly in its application to graph neural networks?
3. How does the reviewer assess the clarity, quality, novelty, and reproducibility of the paper's content?
4. Do you have any q... | Summary Of The Paper
Strengths And Weaknesses
Clarity, Quality, Novelty And Reproducibility | Summary Of The Paper
The paper presents mask autoencoders for meshes. The idea is motivated by masked autoencoders in the case of images. The work treats meshes as graphs with mesh faces as nodes and edges to capture mesh topology/connectivity. Masking is done by randomly removing nodes/faces along with associated edge... |
ICLR | Title
MGMA: Mesh Graph Masked Autoencoders for Self-supervised Learning on 3D Shape
Abstract
We introduce a self-supervised learning model to extract face nodes and global graph embeddings on meshes. We define a model with graph masking on a mesh graph composed of faces to pre-train on self-supervised tasks. We evaluat... | 1. What is the focus and contribution of the paper regarding mesh representation learning?
2. What are the strengths and weaknesses of the proposed method, particularly in terms of its self-supervised nature and use of graph attention layer?
3. Do you have any concerns regarding the training and evaluation process, suc... | Summary Of The Paper
Strengths And Weaknesses
Clarity, Quality, Novelty And Reproducibility | Summary Of The Paper
In this paper, the authors propose a self-supervised method to learn mesh representation. The proposed method uses graph attention layer to build the model and the graph masking strategy to train the model. The learned global feature is trained with the task to reconstruct the point clouds. The tra... |
ICLR | Title
MGMA: Mesh Graph Masked Autoencoders for Self-supervised Learning on 3D Shape
Abstract
We introduce a self-supervised learning model to extract face nodes and global graph embeddings on meshes. We define a model with graph masking on a mesh graph composed of faces to pre-train on self-supervised tasks. We evaluat... | 1. What is the focus of the paper regarding mesh data processing?
2. What are the strengths of the proposed masked autoencoder architecture for 3D meshes?
3. What are the weaknesses of the paper, particularly regarding its novelty and experimental scope?
4. How does the reviewer assess the clarity, quality, novelty, an... | Summary Of The Paper
Strengths And Weaknesses
Clarity, Quality, Novelty And Reproducibility | Summary Of The Paper
The paper proposes a masked autoencoder architecture for 3D meshes. The input mesh is processed to consider the faces as nodes of a connected non-oriented graph. Then, some node of the so obtained graph is masked and passed to attention layers; a final max-pool produces the graph embedding that can... |
ICLR | Title
A TWO-STAGE FRAMEWORK FOR MATHEMATICAL EXPRESSION RECOGNITION
Abstract
Although mathematical expressions (MEs) recognition have achieved great progress, the development of MEs recognition in real scenes is still unsatisfactory. Inspired by the recent work of neutral network, this paper proposes a novel two-stage ... | 1. What is the main contribution of the paper regarding converting images of math expressions into LaTeX?
2. What are the strengths of the proposed two-stage approach compared to end-to-end models?
3. Do you have any concerns about the applicability and interest of the paper for the ICLR community?
4. Are there any spe... | Review | Review
This paper considers the problem of converting an image of a math expression into LaTeX. They note that while the model proposed in Deng et al works well on the IM2LATEX-100K dataset, is doesn't generalize well to equations in real-world settings that you'd have in a photograph or a scan of an equation. They p... |
ICLR | Title
A TWO-STAGE FRAMEWORK FOR MATHEMATICAL EXPRESSION RECOGNITION
Abstract
Although mathematical expressions (MEs) recognition have achieved great progress, the development of MEs recognition in real scenes is still unsatisfactory. Inspired by the recent work of neutral network, this paper proposes a novel two-stage ... | 1. What is the focus of the paper regarding mathematical expression recognition?
2. What are the strengths and weaknesses of the proposed two-stage pipeline?
3. How does the reviewer assess the novelty and significance of the proposed approach compared to prior works?
4. What are some concerns regarding the comparison ... | Review | Review
In this paper the authors propose a two stage pipeline that aims to solve for mathematical expression recognition. The main approach uses the following stages, a detection stage that is based on YoloV3 and a sequence to sequence approach. The authors compare their method against Image2Latex approach (2016) that ... |
ICLR | Title
A TWO-STAGE FRAMEWORK FOR MATHEMATICAL EXPRESSION RECOGNITION
Abstract
Although mathematical expressions (MEs) recognition have achieved great progress, the development of MEs recognition in real scenes is still unsatisfactory. Inspired by the recent work of neutral network, this paper proposes a novel two-stage ... | 1. What is the focus of the paper in terms of mathematical expression recognition?
2. What are the strengths of the proposed approach, particularly in its two-stage framework?
3. Do you have any concerns or suggestions regarding the paper's content, formulas, or references? | Review | Review
This paper proposes to recognise a mathmatical expression using a two-stage framework, including object detection by YOLOv3, and encoder-decoder based translation. The paper is written well, and easy to read. In the experiments, the recognition and translation methods both work well on Homologous and non-Homolog... |
ICLR | Title
Carousel Memory: Rethinking the Design of Episodic Memory for Continual Learning
Abstract
Continual Learning (CL) is an emerging machine learning paradigm that aims to learn from a continuous stream of tasks without forgetting knowledge learned from the previous tasks. To avoid performance decrease caused by forg... | 1. What is the focus of the paper regarding continual learning?
2. What are the strengths of the proposed approach, particularly in terms of its clarity and experimental evaluation?
3. What are the weaknesses of the paper, especially regarding its contributions and novelty?
4. How does the reviewer assess the significa... | Summary Of The Paper
Review | Summary Of The Paper
This paper proposes the use of an external storage in addition to the episodic memory in continual learning. The authors describe a procedure for executing training and data transfer between the RAM and the external storage asynchronously, and three swapping policies to decide which training instan... |
ICLR | Title
Carousel Memory: Rethinking the Design of Episodic Memory for Continual Learning
Abstract
Continual Learning (CL) is an emerging machine learning paradigm that aims to learn from a continuous stream of tasks without forgetting knowledge learned from the previous tasks. To avoid performance decrease caused by forg... | 1. What is the focus of the paper regarding continual learning?
2. What are the strengths of the proposed CarM approach, particularly its novelty and practicality?
3. What are the weaknesses of the paper, especially regarding the comments in the Abstract and Introduction sections?
4. Do you have any concerns about the ... | Summary Of The Paper
Review | Summary Of The Paper
This paper introduces a new episodic memory (EM) management technique CarM in continual learning. Unlike previous approaches, the authors show the hierarchical memory architecture which consists of EM and external storage. At training time, the main model that learns the tasks from stream data util... |
ICLR | Title
Carousel Memory: Rethinking the Design of Episodic Memory for Continual Learning
Abstract
Continual Learning (CL) is an emerging machine learning paradigm that aims to learn from a continuous stream of tasks without forgetting knowledge learned from the previous tasks. To avoid performance decrease caused by forg... | 1. What is the focus of the paper regarding continual learning?
2. What is the intuition behind the proposed approach, and how does it differ from previous methods?
3. What are the strengths and weaknesses of the introduced Episodic Storage, and how does it impact performance?
4. How do data swapping policies contribut... | Summary Of The Paper
Review | Summary Of The Paper
The paper proposed a new strategy for constructing the replay buffer during continual learning. By assuming the split of memories in a continual learner as the large & slow and the small & fast, the authors newly introduce Episodic Storage that memorizes a large number of past tasks' instances, whi... |
ICLR | Title
Carousel Memory: Rethinking the Design of Episodic Memory for Continual Learning
Abstract
Continual Learning (CL) is an emerging machine learning paradigm that aims to learn from a continuous stream of tasks without forgetting knowledge learned from the previous tasks. To avoid performance decrease caused by forg... | 1. What if you use a bigger memory buffer instead of relying on storage?
2. How does the size and variability of the buffer impact performance?
3. What is the actual size of the storage memory used in the experiments?
4. Why do random, entropy, and dynamic policies have similar results?
5. Does the swapping policy impr... | Summary Of The Paper
Review | Summary Of The Paper
The paper proposes Carousel Memory (CarM), a new design for episodic memory (EM) in continual learning (CL) systems based on replay or rehearsal of previously observed samples.
EM buffers are stored in high-speed memory for fast retrieval but are usually limited in size and the number of samples th... |
ICLR | Title
Improving Model Consistency of Decentralized Federated Learning via Sharpness Aware Minimization and Multiple Gossip Approaches
Abstract
To mitigate the privacy leakages and reduce the communication burden of Federated Learning (FL), decentralized FL (DFL) discards the central server and each client only communic... | 1. What is the focus and contribution of the paper on decentralized federated learning?
2. What are the strengths of the proposed approach, particularly in terms of its theoretical soundness and experimental results?
3. What are the weaknesses of the paper, especially regarding its novelty and originality?
4. How does ... | Summary Of The Paper
Strengths And Weaknesses
Clarity, Quality, Novelty And Reproducibility | Summary Of The Paper
The paper proposes a new algorithm DFedSAM within the decentralized federated learning framework, where communications between servers are only performed within local neighborhood. To tackle the sharper landscape generated by the decentralization, the algorithm adapts a sharpness aware minimization... |
ICLR | Title
Improving Model Consistency of Decentralized Federated Learning via Sharpness Aware Minimization and Multiple Gossip Approaches
Abstract
To mitigate the privacy leakages and reduce the communication burden of Federated Learning (FL), decentralized FL (DFL) discards the central server and each client only communic... | 1. What is the focus and contribution of the paper on decentralized learning?
2. What are the strengths of the proposed approach, particularly in terms of its application of SAM?
3. What are the weaknesses of the paper, especially regarding its connection to prior works?
4. Do you have any concerns about the scalabilit... | Summary Of The Paper
Strengths And Weaknesses
Clarity, Quality, Novelty And Reproducibility | Summary Of The Paper
This paper proposes to apply SAM in the decentralized learning scenario to alleviate the distribution shift, termed DFedSAM. Convergence results are provided for smooth non-convex objectives under a bounded gradient assumption. Numerical experiments are conducted on several datasets.
Strengths And... |
ICLR | Title
Improving Model Consistency of Decentralized Federated Learning via Sharpness Aware Minimization and Multiple Gossip Approaches
Abstract
To mitigate the privacy leakages and reduce the communication burden of Federated Learning (FL), decentralized FL (DFL) discards the central server and each client only communic... | 1. What is the main contribution of the paper regarding sharpness-aware minimization in decentralized federated learning?
2. What are the strengths and weaknesses of the proposed approach, particularly in its theoretical analysis?
3. Do you have any concerns regarding the presentation and relevance of the theory presen... | Summary Of The Paper
Strengths And Weaknesses
Clarity, Quality, Novelty And Reproducibility | Summary Of The Paper
Sharpness-aware minimization (SAM) has recently been shown to improve various aspects of deep learning. In this paper, the authors show empirically that SAM also helps to improve the performance in decentralized federated learning.
A main contribution of this paper is a theoretical analysis of thei... |
ICLR | Title
Improving Model Consistency of Decentralized Federated Learning via Sharpness Aware Minimization and Multiple Gossip Approaches
Abstract
To mitigate the privacy leakages and reduce the communication burden of Federated Learning (FL), decentralized FL (DFL) discards the central server and each client only communic... | 1. What is the focus of the paper, and what are the contributions of the proposed approach?
2. What are the strengths and weaknesses of the paper regarding its assumptions, theoretical analysis, and comparison with other works?
3. How does the reviewer assess the clarity, quality, novelty, and reproducibility of the pa... | Summary Of The Paper
Strengths And Weaknesses
Clarity, Quality, Novelty And Reproducibility | Summary Of The Paper
This paper combines sharpness-aware minimization (SAM) with decentralized SGD. It established the convergence rate and demonstrated that it could improve the generalization performance.
However, the novelty is incremental, some assumptions are too strong, it missed some important literature.
Stren... |
ICLR | Title
Object-Contrastive Networks: Unsupervised Object Representations
Abstract
Discovering objects and their attributes is of great importance for autonomous agents to effectively operate in human environments. This task is particularly challenging due to the ubiquitousness of objects and all their nuances in perceptu... | 1. What is the main contribution of the paper regarding unsupervised feature learning?
2. What are the strengths and weaknesses of the proposed approach compared to current unsupervised feature learning methods?
3. How does the reviewer assess the experimental setup and comparisons with other works?
4. Are there any mi... | Review | Review
Summary:
This paper aim for learning a feature representation from video sequences captured from a scene from different view points. The proposed approach is tested on a table top scenario for synthetic and real scenes. Pairs of frames from captured video is selected, then a pre-trained object detector finds the... |
ICLR | Title
Object-Contrastive Networks: Unsupervised Object Representations
Abstract
Discovering objects and their attributes is of great importance for autonomous agents to effectively operate in human environments. This task is particularly challenging due to the ubiquitousness of objects and all their nuances in perceptu... | 1. What is the main contribution of the paper on unsupervised representation learning for visual inputs?
2. What are the strengths and weaknesses of the proposed method, particularly regarding its technical novelty and performance compared to baselines?
3. How does the reviewer assess the clarity, quality, novelty, and... | Review | Review
In this paper, an unsupervised representation learning method for visual inputs is proposed. The proposed method incorporates a metric learning approach that pulls nearest neighbor pairs of image patches close to each other in the embedding space while pushing apart other pairs. The train and test scenarios are ... |
ICLR | Title
Object-Contrastive Networks: Unsupervised Object Representations
Abstract
Discovering objects and their attributes is of great importance for autonomous agents to effectively operate in human environments. This task is particularly challenging due to the ubiquitousness of objects and all their nuances in perceptu... | 1. What is the main contribution of the paper regarding self-supervised learning?
2. What are the strengths and weaknesses of the proposed approach?
3. How does the reviewer assess the significance of the problem setup and the chosen approach?
4. What are some concerns regarding the experimental results and their impli... | Review | Review
This paper explored self-supervised learning of object representations. The main idea is to encourage objects with similar features to get further ‘attracted’ to each other. The authors demonstrated that the system works on real objects with simple geometry.
The problem of self-supervised learning from video d... |
ICLR | Title
SGD Learns One-Layer Networks in WGANs
Abstract
Generative adversarial networks (GANs) are a widely used framework for learning generative models. Wasserstein GANs (WGANs), one of the most successful variants of GANs, require solving a minmax optimization problem to global optimality, but are in practice successf... | 1. What are the concerns regarding the choice of discriminator in the paper?
2. Why does the reviewer find the title of the paper misleading?
3. What are the strengths and weaknesses of the paper's analysis of simplified discriminators?
4. How does the reviewer assess the novelty and significance of the paper's contrib... | Review | Review
The authors provide a long text to justify their contributions and I have read it thoroughly. Unfortunately, I find the responses don't really address my concerns.
My major concern is that I cannot understand how quadratic discriminator can be treated as WGAN. The authors replied that the regularization conside... |
ICLR | Title
SGD Learns One-Layer Networks in WGANs
Abstract
Generative adversarial networks (GANs) are a widely used framework for learning generative models. Wasserstein GANs (WGANs), one of the most successful variants of GANs, require solving a minmax optimization problem to global optimality, but are in practice successf... | 1. What is the focus of the review, and what are the reviewer's concerns regarding the paper?
2. What are the strengths and weaknesses of the paper according to the reviewer?
3. Does the reviewer have any questions about the paper's content or conclusions?
4. How does the reviewer assess the novelty and significance of... | Review | Review
I have read the authors response. In the response the authors clarified the contributions of this paper. I agree with the authors that the analysis of gradient descent-ascent is a difficult problem, and the optimization results given in this paper is a contribution of importance. Because of this I have improved ... |
ICLR | Title
SGD Learns One-Layer Networks in WGANs
Abstract
Generative adversarial networks (GANs) are a widely used framework for learning generative models. Wasserstein GANs (WGANs), one of the most successful variants of GANs, require solving a minmax optimization problem to global optimality, but are in practice successf... | 1. What is the main contribution of the paper regarding Stochastic Gradient Descent-Ascent and WGAN?
2. What are the limitations of the paper, particularly in the settings of the discriminator?
3. How does the reviewer assess the theoretical analysis and experimental results presented in the paper?
4. What additional i... | Review | Review
In this paper, the authors attempt to prove that the Stochastic Gradient Descent-Ascent could converge to a global solution to the min-max problem of WGAN, in the setting of a one-layer generator and simple discriminator. They also show that the linear discriminator could be used to learn the marginal distributi... |
ICLR | Title
Implicit Bias of Large Depth Networks: a Notion of Rank for Nonlinear Functions
Abstract
We show that the representation cost of fully connected neural networks with homogeneous nonlinearities which describes the implicit bias in function space of networks with L2-regularization or with losses such as the cross-e... | 1. What are the main contributions and key concepts discussed in the paper regarding deep neural networks?
2. What are the strengths of the proposed approach, particularly in terms of its notation, organization, and theoretical analysis?
3. Do you have any concerns or questions regarding the paper's content, such as as... | Summary Of The Paper
Strengths And Weaknesses
Clarity, Quality, Novelty And Reproducibility | Summary Of The Paper
This paper studies the rank behavior of deep neural networks with three different types of rank definitions: the maximum of network Jacobi rank, bottlenet rank, and representation cost. The authors rigorously demonstrate the intrinsic connections among those three concepts, revealing a fascinating ... |
ICLR | Title
Implicit Bias of Large Depth Networks: a Notion of Rank for Nonlinear Functions
Abstract
We show that the representation cost of fully connected neural networks with homogeneous nonlinearities which describes the implicit bias in function space of networks with L2-regularization or with losses such as the cross-e... | 1. What is the focus of the paper regarding deep neural networks?
2. What are the strengths and weaknesses of the proposed approach?
3. How does the reviewer assess the relevance of the theory in practical networks?
4. Are there any minor questions or imprecisions in the paper that the reviewer would like to bring atte... | Summary Of The Paper
Strengths And Weaknesses
Clarity, Quality, Novelty And Reproducibility | Summary Of The Paper
the authors consider the implicit bias of deep neural networks with homogeneous activations and linear layers, trained to minimize the square loss against a given piecewise linear target function with
ℓ
2
regularization. previous works have explored the functions achieving the minimum "representati... |
ICLR | Title
Implicit Bias of Large Depth Networks: a Notion of Rank for Nonlinear Functions
Abstract
We show that the representation cost of fully connected neural networks with homogeneous nonlinearities which describes the implicit bias in function space of networks with L2-regularization or with losses such as the cross-e... | 1. What are the key contributions and strengths of the paper regarding the introduction of new notions of rank for nonlinear functions?
2. What are the weaknesses and limitations of the paper, particularly in its assumptions and experimental designs?
3. How does the reviewer assess the clarity, quality, novelty, and re... | Summary Of The Paper
Strengths And Weaknesses
Clarity, Quality, Novelty And Reproducibility | Summary Of The Paper
This paper introduces two new notions of rank for nonlinear functions (Jacobian rank and bottleneck rank). These definitions satisfy a set of properties of matrix ranks and thus generalize this classical notion. Moreover, under these rank notions, there exists regimes (large depth, large sample siz... |
ICLR | Title
Implicit Bias of Large Depth Networks: a Notion of Rank for Nonlinear Functions
Abstract
We show that the representation cost of fully connected neural networks with homogeneous nonlinearities which describes the implicit bias in function space of networks with L2-regularization or with losses such as the cross-e... | 1. What are the main contributions and novel aspects introduced by the paper regarding nonlinear functions and their ranks?
2. What are the strengths and weaknesses of the proposed approach, particularly in terms of its theoretical analysis and practical implications?
3. How does the reviewer assess the clarity, qualit... | Summary Of The Paper
Strengths And Weaknesses
Clarity, Quality, Novelty And Reproducibility | Summary Of The Paper
The paper introduces two notions of rank for nonlinear functions, the Jacobian rank, and the bottleneck rank. The authors then consider fully connected neural networks with homogeneous nonlinearities. They first show that for
L
→
∞
, the reconstruction cost of any piecewise linear function is sandw... |
ICLR | Title
Implicit Bias of Large Depth Networks: a Notion of Rank for Nonlinear Functions
Abstract
We show that the representation cost of fully connected neural networks with homogeneous nonlinearities which describes the implicit bias in function space of networks with L2-regularization or with losses such as the cross-e... | 1. What is the focus and contribution of the paper on neural network representation?
2. What are the strengths of the proposed approach, particularly in terms of its ability to match low-rank functions?
3. What are the weaknesses of the paper regarding its proofs and explanations?
4. How does the reviewer assess the cl... | Summary Of The Paper
Strengths And Weaknesses
Clarity, Quality, Novelty And Reproducibility | Summary Of The Paper
This paper proposes a notion of rank for non-linear functions, which is defined as the minimum possible
L
2
norms of the weights of a neural network which matches the function, averaged over the layers, and asymptotically where the number of layers tends to infinity. It is conjectured that this not... |
ICLR | Title
Implicit Bias of Large Depth Networks: a Notion of Rank for Nonlinear Functions
Abstract
We show that the representation cost of fully connected neural networks with homogeneous nonlinearities which describes the implicit bias in function space of networks with L2-regularization or with losses such as the cross-e... | 1. What is the focus of the paper regarding deep homogeneous nonlinear networks?
2. What are the strengths of the proposed approach, particularly in terms of its novel perspective and practical implications?
3. Do you have any concerns or questions regarding the paper's results and their implications?
4. How does the r... | Summary Of The Paper
Strengths And Weaknesses
Clarity, Quality, Novelty And Reproducibility | Summary Of The Paper
This paper studies the representation cost of piecewise linear functions by deep homogeneous nonlinear networks.
The representation cost of f is defined as R(f) = \min_{W : f_W = f} ||W||^2, where ||.|| is the L2 norm of the parameters and f_W is the neural network parametrized by W. This represent... |
ICLR | Title
Unified Principles For Multi-Source Transfer Learning Under Label Shifts
Abstract
We study the label shift problem in multi-source transfer learning and derive new generic principles. Our proposed framework unifies the principles of conditional feature alignment, label distribution ratio estimation and domain rel... | 1. What is the focus and contribution of the paper regarding multi-source transfer learning?
2. What are the strengths and weaknesses of the proposed algorithm, particularly in its theoretical analysis?
3. Do you have any concerns regarding the assumptions and limitations of the main theorem?
4. How does the quality of... | Review | Review
Summary This paper aims to provide a unified principle for multi-source transfer learning under label shifts. Based on this principle, this paper claims that a unified algorithm is proposed for various multi-source label shift transfer scenarios: learning with limited target data, unsupervised domain adaptation ... |
ICLR | Title
Unified Principles For Multi-Source Transfer Learning Under Label Shifts
Abstract
We study the label shift problem in multi-source transfer learning and derive new generic principles. Our proposed framework unifies the principles of conditional feature alignment, label distribution ratio estimation and domain rel... | 1. What are the contributions and novel aspects of the paper regarding multi-source transfer learning and label shift problem?
2. What are the strengths and weaknesses of the proposed WADN algorithm, particularly in its ability to outperform SOTA methods in certain scenarios?
3. How does the reviewer assess the clarity... | Review | Review
In this paper, the authors focus on the label shift problem in multi-source transfer learning and derive new generic principles to control the target generalization risk. They propose a framework that unifies the principles of conditional feature alignment, label distribution ratio estimation, and domain relatio... |
ICLR | Title
Unified Principles For Multi-Source Transfer Learning Under Label Shifts
Abstract
We study the label shift problem in multi-source transfer learning and derive new generic principles. Our proposed framework unifies the principles of conditional feature alignment, label distribution ratio estimation and domain rel... | 1. What is the main contribution of the paper in unsupervised domain adaptation?
2. What are the strengths of the proposed approach, particularly in its ability to handle different scenarios?
3. What are the weaknesses of the paper regarding its comparisons with other works?
4. How does the reviewer assess the clarity,... | Review | Review
This paper has made a good attempt to provide a unified approach for unsupervised domain adaptation. The proposed approach is applicable to three scenarios which have been traditionally treated as three separate problems. The three problems that are treated in a unified way are Unsupervised Domain Adaptation (UD... |
ICLR | Title
Unified Principles For Multi-Source Transfer Learning Under Label Shifts
Abstract
We study the label shift problem in multi-source transfer learning and derive new generic principles. Our proposed framework unifies the principles of conditional feature alignment, label distribution ratio estimation and domain rel... | 1. What is the main contribution of the paper regarding label shift in multi-source transfer learning?
2. What are the strengths and weaknesses of the proposed framework for learning with no target labels and limited target labels?
3. How does the paper compare to other methods, particularly DANN and CDANN, in terms of... | Review | Review
Summary: The paper is concerned with label shift in multi-source transfer learning setup. In particular authors look into target shift without assuming conditional distributions to be the same in source and target. They propose a unified frameworkthat can be used for learning with no target labels and limited ta... |
ICLR | Title
Deep Double Descent: Where Bigger Models and More Data Hurt
Abstract
We show that a variety of modern deep learning tasks exhibit a “double-descent” phenomenon where, as we increase model size, performance first gets worse and then gets better. Moreover, we show that double descent occurs not just as a function o... | 1. What is the focus of the paper, and what does it aim to achieve?
2. What is the significance of the concept of Effective Model Complexity (EMC)?
3. How does the paper empirically demonstrate the double descent phenomenon's dependence on EMC?
4. What insights does the paper offer regarding the observed phenomena?
5. ... | Review | Review
The paper defines the effective model complexity(EMC) that defines the complexity of the model. EMC depends on several factors such as data distribution and architecture of the classifier.
The paper empirically shows that the double descent phenomenon occurs as a function of EMC.
The paper provides interest... |
ICLR | Title
Deep Double Descent: Where Bigger Models and More Data Hurt
Abstract
We show that a variety of modern deep learning tasks exhibit a “double-descent” phenomenon where, as we increase model size, performance first gets worse and then gets better. Moreover, we show that double descent occurs not just as a function o... | 1. What is the focus of the paper regarding neural networks?
2. What are the strengths and weaknesses of the paper's empirical study?
3. How does the paper relate to prior works on double descent behavior in neural networks?
4. Are there any inconsistencies or inaccuracies in the paper's discussion of effective model c... | Review | Review
This paper provides a valuable and detailed empirical study of the double descent behaviour in neural networks. It investigates presence of this behaviour in a range of neural network architectures and apart of identifying it as a function of the model size it also identifies it as a function of training time wh... |
ICLR | Title
Deep Double Descent: Where Bigger Models and More Data Hurt
Abstract
We show that a variety of modern deep learning tasks exhibit a “double-descent” phenomenon where, as we increase model size, performance first gets worse and then gets better. Moreover, we show that double descent occurs not just as a function o... | 1. What is the main contribution of the paper regarding the double descent phenomenon?
2. What are the strengths of the paper, particularly in its simulations and observations?
3. Do you have any concerns or comments regarding the paper's content or references?
4. How does the reviewer assess the novelty and generality... | Review | Review
I do not have much to say about the paper except that I like the sumilation, and found rather interesting. It shows a rather extensive set of simulations, that enrich the observations of the so-called double descent phenomena, and shows empirically its apparent generality. Also, the Effective Model Complexity se... |
ICLR | Title
Fair Federated Learning via Bounded Group Loss
Abstract
Fair prediction across protected groups is an important constraint for many federated learning applications. However, prior work studying group fair federated learning lacks formal convergence or fairness guarantees. In this work we propose a general framewo... | 1. What is the focus and contribution of the paper regarding fair federated learning?
2. What are the strengths of the proposed approach, particularly in terms of allowing direct specification of target group-specific loss minimums?
3. What are the weaknesses of the paper, especially regarding the novelty of BGL and it... | Summary Of The Paper
Strengths And Weaknesses
Clarity, Quality, Novelty And Reproducibility | Summary Of The Paper
The paper discusses the use of bounded group loss (BGL) in the context of fair federated learning. The core objective is to learn a classifier that satisfies some predefined constraints on the expected loss function of each group on average across all clients. It is expected that the client-conditi... |
ICLR | Title
Fair Federated Learning via Bounded Group Loss
Abstract
Fair prediction across protected groups is an important constraint for many federated learning applications. However, prior work studying group fair federated learning lacks formal convergence or fairness guarantees. In this work we propose a general framewo... | 1. What are the strengths and weaknesses of the proposed group federated learning algorithm?
2. How does the reviewer assess the privacy leakage and secrecy of the proposed algorithm?
3. Does the reviewer have concerns regarding the use of BGL for certain tasks?
4. Why do some of the experimental results appear unusual... | Summary Of The Paper
Strengths And Weaknesses
Clarity, Quality, Novelty And Reproducibility | Summary Of The Paper
The paper proposes a group federated learning algorithm with formal convergence/BGL guarantees.
Strengths And Weaknesses
-Privacy leakage/secrecy of the proposed algorithm is not discussed. It is unclear how much privacy leakage occurs. The proposed algorithm requires the exchange of additional in... |
ICLR | Title
Fair Federated Learning via Bounded Group Loss
Abstract
Fair prediction across protected groups is an important constraint for many federated learning applications. However, prior work studying group fair federated learning lacks formal convergence or fairness guarantees. In this work we propose a general framewo... | 1. What is the focus of the paper regarding fair learning objectives?
2. What are the strengths and weaknesses of the proposed method in terms of its technical idea and selection of loss functions?
3. Do you have any concerns or questions about the lack of discussions on techniques and comparisons with other works in t... | Summary Of The Paper
Strengths And Weaknesses
Clarity, Quality, Novelty And Reproducibility | Summary Of The Paper
The paper proposes a fair learning objective for federated settings via Bounded Group Loss. The authors propose a scalable federated solver to find an approximate saddle point for the objective. Theoretically, they provide convergence and fairness guarantees for the method. Empirically, they show t... |
ICLR | Title
Fair Federated Learning via Bounded Group Loss
Abstract
Fair prediction across protected groups is an important constraint for many federated learning applications. However, prior work studying group fair federated learning lacks formal convergence or fairness guarantees. In this work we propose a general framewo... | 1. What is the focus and contribution of the paper regarding fair federated learning?
2. What are the strengths and weaknesses of the proposed approach, particularly in terms of its fairness criterion and theoretical guarantees?
3. Do you have any concerns about the choice of fairness metric and its potential impact on... | Summary Of The Paper
Strengths And Weaknesses
Clarity, Quality, Novelty And Reproducibility | Summary Of The Paper
This paper studies fair federated learning (FL) where each group of clients are guaranteed to have the same loss function value upper bound. Theoretical guarantees for both model convergence (for convex loss functions) and fairness are provided, together with experimental results that support the c... |
ICLR | Title
On Representation Learning in the First Layer of Deep CNNs and the Dynamics of Gradient Descent
Abstract
It has previously been reported that the representation that is learned in the first layer of deep CNNs is very different from the initial representation and highly consistent across initialization and archite... | 1. What is the main contribution of the paper regarding the consistency of CNN representations?
2. What are the strengths and weaknesses of the proposed measure called energy profiles?
3. How does the reviewer assess the clarity, quality, novelty, and reproducibility of the paper's content?
4. What are the implications... | Summary Of The Paper
Strengths And Weaknesses
Clarity, Quality, Novelty And Reproducibility | Summary Of The Paper
This paper studies the empirical phenomenon that CNNs learn consistent representations in the first layer across different experimental settings (architecture, initializations, etc.). The goal is to understand the source of this consistency. To quantitatively measure consistency, the authors sugges... |
ICLR | Title
On Representation Learning in the First Layer of Deep CNNs and the Dynamics of Gradient Descent
Abstract
It has previously been reported that the representation that is learned in the first layer of deep CNNs is very different from the initial representation and highly consistent across initialization and archite... | 1. What is the focus of the paper regarding the analysis of CNN representations?
2. What are the strengths and weaknesses of the proposed approach, particularly in its experimental results and conclusions?
3. Do you have any concerns or questions about the paper's methodology, notation, and theoretical setting?
4. How ... | Summary Of The Paper
Strengths And Weaknesses
Clarity, Quality, Novelty And Reproducibility | Summary Of The Paper
The paper aims to analyze the representation learned in the first layer of CNNs. Existing work identifies that such representations are highly consistently across architectures and initializations. This paper introduces a functional called energy profile aimed to quantifying this consistence, and r... |
ICLR | Title
On Representation Learning in the First Layer of Deep CNNs and the Dynamics of Gradient Descent
Abstract
It has previously been reported that the representation that is learned in the first layer of deep CNNs is very different from the initial representation and highly consistent across initialization and archite... | 1. What is the focus of the paper regarding CNNs?
2. What are the strengths and weaknesses of the proposed approach?
3. Do you have any concerns or questions about the presented results and their implications?
4. How do you assess the clarity, quality, novelty, and reproducibility of the paper's content? | Summary Of The Paper
Strengths And Weaknesses
Clarity, Quality, Novelty And Reproducibility | Summary Of The Paper
The paper shows that even when the first layers are kept random, CNNs are able to learn with the remaining layers with the results comparable with full learning, also that highly consistent representation is learned in the first layer when the true labels are replaced with random labels.
Strengths... |
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