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In this paper, the author investigates how to utilize large-scale human video to train dexterous robot manipulation skills. To leverage the information from the Internet videos, the author proposes a handful of techniques to pre-process the video data to extract the action information. Then the network is trained on th... | This paper studies how to learn dexterous manipulation from human videos. In the initial review, the reviewer appreciated the direction and real-world experiment but also raised concerns about the need of special sensor for tracking. During rebuttal, the authors effectively addressed this concern by providing addit... |
**Summary of contributions:** This paper proposes a new framework to design new loss for GANs. The authors show that their framework is quite general and encompass a number of existing approaches (e.g. the original GAN formulation, hinge loss, etc..), they also propose a categorization in three different classes and de... | This paper proposed a new family of losses for GANs and showed that this family is quite general and encompasses a number of existing losses as well as some new loss functions. The paper compared experimentally the existing losses and the new proposed losses. But the benefit of this family is not clear theoretically, a... |
This paper addresses the problem of MoE routing under the cases of different network topologies by allocating another abstraction layer for the topology and designing an auxiliary objective to optimize. Experiments show very good improvement in terms of speed compared to strong baselines. Strength: 1. The paper offer... | Mixture-of-Expert (MoE) models have demonstrated a lot of success recently. To further improve upon the existing literature this paper studies MoE routing for different network topologies. This is essentially to deal with the communication overhead of MoE training. The strategy is to add another layer on top for the to... |
This paper discusses applications of variants of RNNs and Gated CNN to acoustic modeling in embedded speech recognition systems, and the main focus of the paper is computational (memory) efficiency when we deploy the system. The paper well describes the problem of the current LSTM, especially focusing on the recurrent ... | In this work, the authors conduct experiments using variants of RNNs and Gated CNNs on a speech recognition task, motivated by the goal of reducing the computational requirements when deploying these models on mobile devices. While this is an important concern for practical deployment of ASR systems, the main concerns ... |
The authors introduce the problem of telegraphic summarization: given a sentence, we want to reduce its size while retaining its meaning, with no penalty for grammatical mistakes. The main application presented by the author is that of summarizing fictional stories and plays. The setting proposed by the author prescri... | This paper presents methods for telegraphic summarization, a task that generates extremely short summaries. There are concerns about the utility of the task in general, and also the novelty of the modeling framework. There is overall consensus between reviewers regarding the paper's assessment the feedback is lukewar... |
This work tackles the task of forecasting dynamics in different domains simultaneously. Using an encoder which is trained to determine the task, the inferred latent vector is then used to adapt a forecasting network to the task at hand. Experiments on three datasets linked to fluid dynamics are then conducted to assess... | The paper addresses the problem of domain generalization for learning spatio-temporal dynamics. It proposes a solution where an encoder captures some characteristics of a given environment, and a forecaster autoregressively predicts future dynamics conditioned on the characteristics learned by the encoder. Said otherwi... |
The paper studies the Mixture of experts (MoE) architecture which has become popular in NLP recently as a way to increase the capacity of network without increasing depth. The authors aim to develop a theoretical understanding of the MoE model/conditional computation. The authors begin with a formal model for condition... | The paper provides a theoretical analysis of sparsely activated neural networks. They introduce LSH (local sensitive hashing) as a new routing function for theoretical analysis and proved a few results on representation power and inference time. One reviewer pointed out that the theoretical results are expected and do ... |
The paper proposes a new approach to inject knowledge into pre-trained language representation models (PLMs). Instead of tuning the original PLM parameters, the paper plugs in new adapters for knowledge injection to avoid catastrophic forgetting. Pros: * Injecting knowledge into PLMs is an advanced topic. The authors... | The paper augments pre-trained language models by introducing “adapter”, where each adapter is another language model pre-trained for a specific knowledge source (e.g., Wikidata) and an objective (e.g., relation classification). The representation from each adapter is concatenated to the representation from the generic... |
This paper presents a formal analysis for the impact of graph reordering (i.e., ordering the in-memory storage sequence of graph node embeddings) on the cache efficiency of near neighbour searches using near neighbour graphs. The connection of the graph ordering (i.e., memory layout of the graph nodes) and the cache co... | This paper studies how to order in-memory sequences for graph embedding. There was a positive consensus that the studied problem is interesting and results are sufficiently discussed. There were some concerns on missing results, which were addressed during rebuttals. |
This paper introduces the PAC-Bayes Information Bottleneck (PIB). Starting from the generalization bound Eq. 4 which shows that the generalization gap is upper bounded by a function of I(w;S), the authors proposes PIB which has an additional regularization term of \\beta I(w;S). Since the computation of I(w;S) is intra... | This paper revisits the information bottleneck principle, but in terms of the compression inherent in the weights of a neural network, rather than the representation. This gives the resulting IB principle a PAC-Bayes flavor. The key contribution is a generalization bound based on optimizing the objective dictated by th... |
I liked this paper quite a lot. Although this paper does not belong to my area of expertise, I was able to understand the paper clearly because of its lucid exposition. Experimentally, the authors show a novel GNN design with an attention module that has comparable performance to the MLP and outperforms other GNN desig... | This paper proposes a GNN that uses global attention based on graph wavelet transform for more flexible and data-dependent GNN feature aggregation without the assumption of local homophily. Three reviewers gave conflicting opinions on this paper. The reviewer claiming rejection questioned the novelty of the paper and ... |
The paper proposes a novel framework for semi-supervised learning, that solves two issues of previous methods: 1) over-reliance on labeled data and 2) error accumulation. It shows that jointly solving the main task together with another task (that discriminates whether the data label is real or not) leads to better per... | This paper proposes a new method for the important problem of semi-supervised learning. This method relies on an auxiliary task, label observability prediction, to weight the examples according to the confidence in their pseudo-labels, so as to avoid the propagation of errors encountered in self-training. Limited exper... |
The main goal of this paper is to introduce a simple methodology for optimizing transformer based models for efficiency and effectiveness. The paper introduces two main ideas: 1)A top-down strategy for pruning components of a transformer model: Given a specific focus, say speed, the strategy is to consider pruning l... | This paper introduces a set of techniques that can be used to obtain smaller models on downstream tasks, when fine-tuning large pre-trained models such as BERT. Some reviewers have noted the limited technical novelty of the paper, which can be seen more as a combination of existing methods. This should not be a reason ... |
The paper is a natural extension of [1] which shows the importance of spectral normalization to encourage diversity of the discriminator weights in a GAN. A simple and effective parametrization of the weights similar to SVD is used: W = USV^T is used along with an orthonormal penalty on U and V and spectral penalty to ... | All the reviewers agree that the paper has an interesting idea on regularizing the spectral norm of the weight matrices in GANs, and a generalization bound has been shown. The empirical result shows that indeed regularization improves the performance of the GANs. Based on these the AC suggested acceptance. |
The paper is about a method for synthesizing binaural audio from a mono recording of a single speaker's speech. First, I think the title is too general. The paper does not attempt to convert all possible sounds, but it tries to convert a single speaker's monaural speech signal to binaural audio where the speaker is mo... | + Interesting method for binaural synthesis from moving mono-audio + Nice insight into why l2 isn't the best loss for binaural reconstructions. + Interesting architectural choice with nice results. + Nicely motivated and clearly presented idea -- especially after addressing the reviewers comments. I agree with the id... |
Summary: The paper considers the adversarial attacks via a surrogate model constructed using data from a different domain. The authors propose a defense from such attacks by a special kind of adversarial training inspired by the idea of domain adaptation. The idea can be useful but raises a lot of questions, especiall... | The submission considers a new attack model for adversarial perturbation in a framework where the attacker has neither access to the trained model nor the data used for training the model. The submission suggests a"domain adaptation inspired attack": learn a different model on a similar domain and generate the adversar... |
This paper proposes a detailed analysis on pruning heuristics, and its applications to early pruning. It thoroughly analyzed magnitude-based pruning, loss-preservation based pruning, and gradient-norm based pruning. The paper demonstrated the results on CIFAR-10 and CIFAR-100 datasets. it's very timely research to guid... | This paper proposes a broad framework for unifying various pruning approaches and performs detailed analyses to make recommendations about the settings in which various approaches may be most useful. Reviewers were generally excited by the framework and analyses, but had some concerns regarding scale and the paper's fo... |
Summary. This paper aims to explain dropout from the lens of game theoretic interactions. Let x denote the input of a deep neural net (DNN), intuitively, the interaction between two variables x_i and x_j quantifies how much the presence/absence of the j-th variable affects the contribution of the i-th variable to the ... | The paper introduces a game-theoretic framework to improve our understanding of dropout. All reviewers appreciated the contribution of the paper. While they had a number of questions/suggestions, almost all of them were adequately addressed. Three reviewers are satisfied and recommend acceptance, while a lone reviewer ... |
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Dataset Summary
The Meta-Review dataset is a dataset created based on the ORSUM dataset proposed in the paper "Meta-review Generation with Checklist-guided Iterative Introspection" by Zeng et al. Downloaded from their official GitHub Repo: https://github.com/Mankeerat/orsum-meta-review-generation
Supported Tasks and Leaderboards
Multi-Document Summarization
Languages
English
Dataset Structure
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Source Data
Initial Data Collection and Normalization
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Who are the source language producers?
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Annotations
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Personal and Sensitive Information
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