pwc-archive/papers-with-abstracts huggingface dataset:
Exploiting Class Activation Value for Partial-Label Learning
Authors: Fei Zhang, Lei Feng, Bo Han, Tongliang Liu, Gang Niu, Tao Qin, Masashi Sugiyama
Date: 2021-09-29 00:00:00
Proceeding: ICLR 2022 4
Tasks: Multi-class Classification, Partial Label Learning

Partial-label learning (PLL) solves the multi-class classification problem, where each training instance is assigned a set of candidate labels that include the true label. Recent advances showed that PLL can be compatible with deep neural networks, which achieved state-of-the-art performance. However, most of the existing deep PLL methods focus on designing proper training objectives under various assumptions on the collected data, which may limit their performance when the collected data cannot satisfy the adopted assumptions. In this paper, we propose to exploit the learned intrinsic representation of the model to identify the true label in the training process, which does not rely on any assumptions on the collected data. We make two key contributions. As the first contribution, we empirically show that the class activation map (CAM), a simple technique for discriminating the learning patterns of each class in images, is surprisingly better at making accurate predictions than the model itself on selecting the true label from candidate labels. Unfortunately, as CAM is confined to image inputs with convolutional neural networks, we are yet unable to directly leverage CAM to address the PLL problem with general inputs and models. Thus, as the second contribution, we propose the class activation value (CAV), which owns similar properties of CAM, while CAV is versatile in various types of inputs and models. Building upon CAV, we propose a novel method named CAV Learning (CAVL) that selects the true label by the class with the maximum CAV for model training. Extensive experiments on various datasets demonstrate that our proposed CAVL method achieves state-of-the-art performance.
SiT: Simulation Transformer for Particle-based Physics Simulation
Authors: Yidi Shao, Chen Change Loy, Bo Dai
Date: 2021-09-29 00:00:00

Most existing particle-based simulators adopt graph convolutional networks (GCNs) to model the underlying physics of particles. 
However, they force particles to interact with all neighbors without selection, and they fall short in capturing material semantics for different particles, leading to unsatisfactory performance, especially in generalization.
This paper proposes Simulation Transformer (SiT) to simulate particle dynamics with more careful modeling of particle states, interactions, and their intrinsic properties.
Specifically, besides the particle tokens, SiT generates interaction tokens and selectively focuses on essential interactions by allowing both tokens to attend to each other.
In addition, SiT learns material-aware representations by learnable abstract tokens, which will participate in the attention mechanism and boost the generalization capability further.
We evaluate our model on diverse environments, including fluid, rigid, and deformable objects, which cover systems of different complexity and materials.
Without bells and whistles, SiT shows strong abilities to simulate particles of different materials and achieves superior performance and generalization across these environments with fewer parameters than existing methods. Codes and models will be released.
Towards Understanding Data Values: Empirical Results on Synthetic Data
Authors: Danilo Brajovic, Omar de Mitri, Alex Windberger, Marco Huber
Date: 2021-09-29 00:00:00
Tasks: Data Valuation

Understanding the influence of data on machine learning models is an emerging research field. Inspired by recent work in data valuation, we perform several experiments to get an intuition for this influence on a multi-layer perceptron. We generate a synthetic two-dimensional data set to visualize how different valuation methods value data points on a mesh grid spanning the relevant feature space. In this setting, individual data values can be derived directly from the impact of the respective data points on the decision boundary. Our results show that the most important data points are the miss-classified ones. Furthermore, despite performance differences on real world data sets, all investigated methods except one qualitatively agree on the data values derived from our experiments. Finally, we place our results into the recent literature and discuss data values and their relationship to other methods.
Rewardless Open-Ended Learning (ROEL)
Authors: Alexander Quessy, Thomas Stuart Richardson
Date: 2021-09-29 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Unsupervised Reinforcement Learning

Open-ended learning algorithms aim to automatically generate challenges and solutions to an unending sequence of learning opportunities. In Reinforcement Learning (RL) recent approaches to open-ended learning, such as Paired Open-Ended Trailblazer (POET), focus on collecting a diverse set of solutions based on the novelty of an agents pre-defined reward function. In many practical RL tasks defining an effective reward function a priori is often hard and can hinder an agents ability to explore many behaviors that could ultimately be more performant. In this work we combine open-ended learning with unsupervised reinforcement learning to train agents to learn a diverse set of complex skills. We propose a procedure to combine skill-discovery via mutual information, using the POET algorithm as an open-ended framework to teach agents increasingly complex groups of diverse skills. Experimentally we demonstrate this approach yields agents capable of demonstrating identifiable skills over a range of environments, that can be extracted and utilized to solve a variety of tasks.
Offline Reinforcement Learning with In-sample Q-Learning
Authors: Ilya Kostrikov, Ashvin Nair, Sergey Levine
Date: 2021-09-29 00:00:00
Proceeding: ICLR 2022 4
Tasks: D4RL, Offline RL, Q-Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Offline reinforcement learning requires reconciling two conflicting aims: learning a policy that improves over the behavior policy that collected the dataset, while at the same time minimizing the deviation from the behavior policy so as to avoid errors due to distributional shift. This tradeoff is critical, because most current offline reinforcement learning methods need to query the value of unseen actions during training to improve the policy, and therefore need to either constrain these actions to be in-distribution, or else regularize their values. We propose a new offline RL method that never needs to evaluate actions outside of the dataset, but still enables the learned policy to improve substantially over the best behavior in the data through generalization. The main insight in our work is that, instead of evaluating unseen actions from the latest policy, we can approximate the policy improvement step implicitly by treating the state value function as a random variable, with randomness determined by the action (while still integrating over the dynamics to avoid excessive optimism), and then taking a state conditional upper expectile of this random variable to estimate the value of the best actions in that state. This leverages the generalization capacity of the function approximator to estimate the value of the best available action at a given state without ever directly querying a Q-function with this unseen action. Our algorithm alternates between fitting this upper expectile value function and backing it up into a Q-function, without any explicit policy. Then, we extract the policy via advantage-weighted behavioral cloning, which also avoids querying out-of-sample actions. We dub our method in-sample Q-learning (IQL).  IQL is easy to implement, computationally efficient, and only requires fitting an additional critic with an asymmetric L2 loss. IQL demonstrates the state-of-the-art performance on D4RL, a standard benchmark for offline reinforcement learning. We also demonstrate that IQL achieves strong performance fine-tuning using online interaction after offline initialization.
End-to-End Learning of Probabilistic Hierarchies on Graphs
Authors: Daniel Zügner, Bertrand Charpentier, Morgane Ayle, Sascha Geringer, Stephan Günnemann
Date: 2021-09-29 00:00:00
Proceeding: ICLR 2022 4
Tasks: Clustering, Link Prediction

We propose a novel probabilistic model over hierarchies on graphs obtained by continuous relaxation of tree-based hierarchies. We draw connections to Markov chain theory, enabling us to perform hierarchical clustering by efficient end-to-end optimization of quality metrics such as Dasgupta cost or Tree-Sampling Divergence (TSD). We show that our model learns rich, high-quality hierarchies present in 8 real world graphs, including a large graph with 2.3M nodes. Our model consistently outperforms recent as well as strong traditional baselines such as average linkage. We demonstrate that our model also obtains competitive results on link prediction despite not being trained on this task, highlighting the quality of the hierarchies discovered by our model.
Fully Steerable 3D Spherical Neurons
Authors: Pavlo Melnyk, Michael Felsberg, Mårten Wadenbäck
Date: 2021-09-29 00:00:00
Tasks: 3D geometry

Emerging from low-level vision theory, steerable filters found their counterpart in prior work on steerable convolutional neural networks equivariant to rigid transformations. In our work, we propose a steerable feed-forward learning-based approach that consists of spherical decision surfaces and operates on point clouds. Focusing on 3D geometry, we derive a 3D steerability constraint for hypersphere neurons, which are obtained by conformal embedding of Euclidean space and have recently been revisited in the context of learning representations of point sets. Exploiting the rotational equivariance, we show how our model parameters are fully steerable at inference time. We use a synthetic point set and real-world 3D skeleton data to show how the proposed spherical filter banks enable making equivariant and, after online optimization, invariant class predictions for known point sets in unknown orientations.
InstaHide’s Sample Complexity When Mixing Two Private Images
Authors: Baihe Huang, Zhao Song, Runzhou Tao, Ruizhe Zhang, Danyang Zhuo
Date: 2021-09-29 00:00:00
Tasks: Vocal Bursts Valence Prediction

Inspired by InstaHide challenge [Huang, Song, Li and Arora'20], [Chen, Song and Zhuo'20] recently provides one mathematical formulation of InstaHide attack problem under Gaussian images distribution. They show that it suffices to use $O(n_{\mathsf{priv}}^{k_{\mathsf{priv}} - 2/(k_{\mathsf{priv}} + 1)})$ samples to recover one private image in $n_{\mathsf{priv}}^{O(k_{\mathsf{priv}})} + \mathrm{poly}(n_{\mathsf{pub}})$ time for any integer $k_{\mathsf{priv}}$, where $n_{\mathsf{priv}}$ and $n_{\mathsf{pub}}$ denote the number of images used in the private and the public dataset to generate a mixed image sample. Under the current setup for the InstaHide challenge of mixing two private images ($k_{\mathsf{priv}} = 2$), this means $n_{\mathsf{priv}}^{4/3}$ samples are sufficient to recover a private image. In this work, we show that $n_{\mathsf{priv}} \log ( n_{\mathsf{priv}} )$ samples are sufficient (information-theoretically) for recovering all the private images.
GreaseLM: Graph REASoning Enhanced Language Models
Authors: Xikun Zhang, Antoine Bosselut, Michihiro Yasunaga, Hongyu Ren, Percy Liang, Christopher D Manning, Jure Leskovec
Date: 2021-09-29 00:00:00
Proceeding: ICLR 2022 4
Tasks: Knowledge Graphs, Medical Question Answering, MedQA, Negation, Open-Ended Question Answering, Question Answering, World Knowledge

Answering complex questions about textual narratives requires reasoning over both stated context and the world knowledge that underlies it. However, pretrained language models (LM), the foundation of most modern QA systems, do not robustly represent latent relationships between concepts, which is necessary for reasoning. While knowledge graphs (KG) are often used to augment LMs with structured representations of world knowledge, it remains an open question how to effectively fuse and reason over the KG representations and the language context, which provides situational constraints and nuances. In this work, we propose GreaseLM, a new model that fuses encoded representations from pretrained LMs and graph neural networks over multiple layers of modality interaction operations. Information from both modalities propagates to the other, allowing language context representations to be grounded by structured world knowledge, and allowing linguistic nuances (e.g., negation, hedging) in the context to inform the graph representations of knowledge. Our results on three benchmarks in the commonsense reasoning (i.e., CommonsenseQA, OpenbookQA) and medical question answering (i.e., MedQA-USMLE) domains demonstrate that GreaseLM can more reliably answer questions that require reasoning over both situational constraints and structured knowledge, even outperforming models 8x larger.
Noisy Adversarial Training
Authors: Nils Worzyk
Date: 2021-09-29 00:00:00
Tasks: Data Augmentation, image-classification, Image Classification, Self-Supervised Learning

In image classification, data augmentation and the usage of additional data has been shown to increase the efficiency of clean training and the accuracy of the resulting model. However, this does not prevent models from being fooled by adversarial manipulations. To increase the robustness, Adversarial Training (AT) is an easy, yet effective and widely used method to harden neural networks against adversarial inputs. Still, AT is computationally expensive and inefficient in that way, that only one adversarial input per sample of the current batch is created. We propose Noisy Adversarial Training (N-AT), which, for the first time, combines data augmentation in the decision space and adversarial training. By adding random noise to the original adversarial output vector, we create multiple pseudo adversarial instances, thus increasing the data pool for adversarial training. We show that this general idea is applicable to two different learning paradigms, i.e., supervised and self-supervised learning. Using N-AT instead of AT, we can increase the robustness relatively by 1.06\% for small seen attacks. For larger seen attacks, the relative gain in robustness increases up to 89.26\%. When combining a larger corpus of input data with our proposed method, we report an increase of the clean accuracy and for all observed attacks, compared to AT. In self-supervised training, we observe a similar increase in robust accuracy for seen attacks and large unseen attacks, when it comes to the downstream task of image classification. In addition, when the pretrained model is finetuned, we also report a relative gain in clean accuracy between 0.5\% and 1.11\%.
Neural Relational Inference with Node-Specific Information
Authors: Ershad Banijamali
Date: 2021-09-29 00:00:00
Proceeding: ICLR 2022 4
Tasks: Variational Inference

Inferring interactions among entities is an important problem in studying dynamicalsystems, which greatly impacts the performance of downstream tasks, such asprediction.  In this paper, we tackle this problem in a setting where each entitycan potentially have a set of individualized information that other entities cannothave access to. Specifically, we represent the system using a graph in which theindividualized information become node-specific information (NSI). We build ourmodel in the framework of Neural Relation Inference (NRI), where the interactionamong entities are interpretably uncovered using variational inference. We adoptNRI model to incorporate the individualized information by introducingprivatenodesin the graph that represent NSI. Such representation enables us to uncovermore accurate relations among the agents and therefore leads to better performanceon the downstream tasks. Our experiment results over real-world datasets validatethe merit of our proposed algorithm.
Assessing Deep Reinforcement Learning Policies via Natural Corruptions at the Edge of Imperceptibility
Authors: Ezgi Korkmaz
Date: 2021-09-29 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Deep reinforcement learning algorithms have recently achieved significant success in learning high-performing policies from purely visual observations. The ability to perform end-to-end learning from raw high dimensional input alone has led to deep reinforcement learning algorithms being deployed in a variety of fields. Thus, understanding and improving the ability of deep reinforcement learning policies to generalize to unseen data distributions is of critical importance. Much recent work has focused on assessing the generalization of deep reinforcement learning policies by introducing specifically crafted adversarial perturbations to their inputs. In this paper, we approach this problem from another perspective and propose a framework to assess the generalization skills of trained deep reinforcement learning policies. Rather than focusing on worst-case analysis of distribution shift, our approach is based on black-box perturbations that correspond to minimal semantically meaningful natural changes to the environment or the agent's visual observation system ranging from brightness to compression artifacts. We demonstrate that the perceptual similarity distance of the minimal natural perturbations is orders of magnitude smaller than the perceptual similarity distance of the adversarial perturbations to the unperturbed observations (i.e. minimal natural perturbations are perceptually more similar to the unperturbed states than the adversarial perturbations), while causing larger degradation in the policy performance. Furthermore, we investigate state-of-the-art adversarial training methods and show that adversarially trained deep reinforcement learning policies are more sensitive to almost all of the natural perturbations compared to vanilla trained policies. Lastly, we highlight that our framework captures a diverse set of bands in the Fourier spectrum; thus providing a better overall understanding of the policy's generalization capabilities. We believe our work can be crucial towards building resilient and generalizable deep reinforcement learning policies.
Attention-based Feature Aggregation
Authors: Xiongwei Wu, Ee-Peng Lim, Steven Hoi, Qianru Sun
Date: 2021-09-29 00:00:00
Tasks: Instance Segmentation, object-detection, Object Detection, Semantic Segmentation

Capturing object instances in different scales is a long-standing problem in the tasks of visual recognition, e.g., object detection and instance segmentation. The conventional way is to learn scale-invariant features, e.g., by summing up the feature maps output by different layers in the backbone. In this paper, we propose a novel and adaptive feature aggregation module based on attention where the attention parameters can be learned to handle different situations, e.g., adding shallow layers is learned to be conservative to mitigate the effect of noisy pixels, while for deep layers, it tends to be audacious to incorporate high-level semantics. To implement this module, we define two variants of attention: self-attention on the summed-up feature map, and cross-attention between two feature maps before summed up. The former uses the aggregated pixel values to capture global attention (to improve the feature for the next layer of aggregation), while the latter allows attention-based interactions between two features before aggregation. In addition, we apply multi-scale pooling in our attention module to reduce computational costs, and thus call the two variants Multi-Scale Self-Attention (MSSA) and Multi-Scale Cross-Attention (MSCA), respectively. We incorporate each variant into multiple baselines, e.g., the state-of-the-art object recognizer Cascade Mask-RCNN, and evaluate them on MSCOCO and LVIS datasets. Results show our significant improvements over baselines, e.g., boosting Cascade Mask-RCNN by 2.2% for AP^box and 2.7% for AP^mask on the MSCOCO dataset.
Incorporating User-Item Similarity in Hybrid Neighborhood-based Recommendation System
Authors: Nghia Duong Tan, Giang Do Truong, Nam Doan Nguyen, Nghia Cao Tuan, Hoang Tran Manh, Minh Nguyen Duc, Hieu Dang Quang
Date: 2021-09-29 00:00:00
Tasks: Collaborative Filtering, Recommendation Systems

Modern hybrid recommendation systems require a sufficient amount of data. However, several internet privacy issues make users skeptical about sharing their personal information with online service providers. This work introduces various novel methods utilizing the baseline estimate to learn user interests from their interactions. Subsequently, extracted user feature vectors are implemented to estimate the user-item correlations, providing an additional fine-tuning factor for neighborhood-based collaborative filtering systems. Comprehensive experiments show that utilizing the user-item similarity can boost the accuracy of hybrid neighborhood-based systems by at least $2.11\%$ while minimizing the need for tracking users' digital footprints.
Succinct Compression: Near-Optimal and Lossless Compression of Deep Neural Networks during Inference Runtime
Authors: Yicun Duan, Xiangjun Peng
Date: 2021-09-29 00:00:00
Tasks: Quantization

Recent advances in Deep Neural Networks (DNN) compression (e.g. pruning, quantization and etc.) significantly reduces the amount of space consumption for storage, making them easier to deploy in low-cost devices. However, those techniques do not keep the compressed representation during inference runtime, which incurs significant overheads in terms of both performance and space consumption. We introduce ``Succinct Compression”, a three-stage framework to enable DNN inference with near-optimal compression and much better performance during inference runtime. The key insight of our method leverages the concept of \textit{Succinct Data Structures}, which supports fast queries directly on compressed representation without decompression. Our method first transforms DNN models as our proposed formulations in either Element-wise or Block-wise manner, so that \textit{Succinct Data Structures} can take advantage of. Then, our method compresses transformed DNN models using \textit{Succinct Data Structures}. Finally, our method exploits our specialized execution pipelines for different model formulations, to retrieve relevant data for DNN inference. Our experimental results show that, our method keeps near-optimal compression, and achieves at least 8.7X/11.5X speedup on AlexNet/VGG-16 inference, compared with Huffman Coding. We also experimentally show that our method is quite synergistic with Pruning and Quantization.