pwc-archive/papers-with-abstracts huggingface dataset:
Explaining Point Processes by Learning Interpretable Temporal Logic Rules
Authors: Shuang Li, Mingquan Feng, Lu Wang, Abdelmajid Essofi, Yufeng Cao, Junchi Yan, Le Song
Date: 2021-09-29 00:00:00
Proceeding: ICLR 2022 4
Tasks: Point Processes

We propose a principled method to learn a set of human-readable logic rules to explain temporal point processes. 
We assume that the generative mechanisms underlying the temporal point processes are governed by a set of first-order temporal logic rules, as a compact representation of domain knowledge. Our method formulates the rule discovery process from noisy event data as a maximum likelihood problem, and designs an efficient and tractable branch-and-price algorithm to progressively search for new rules and expand existing rules. The proposed algorithm alternates between the rule generation stage and the rule evaluation stage, and uncovers the most important collection of logic rules within a fixed time limit for both synthetic and real event data. In a real healthcare application, we also had human experts (i.e., doctors) verify the learned temporal logic rules and provide further improvements. These expert-revised interpretable rules lead to a point process model which outperforms previous state-of-the-arts for symptom prediction, both in their occurrence times and types.
Learning Minimal Representations with Model Invariance
Authors: Manan Tomar, Amy Zhang, Matthew E. Taylor
Date: 2021-09-29 00:00:00
Tasks: model, Self-Supervised Learning

Sparsity has been identified as an important characteristic in learning neural networks that generalize well, forming the key idea in constructing minimal representations. Minimal representations are ones that only encode information required to predict well on a task and nothing more. In this paper we present a powerful approach to learning minimal representations. Our method, called ModInv or model invariance, argues for learning using multiple predictors and a single  representation, creating a bottleneck architecture. Predictors' learning landscapes are diversified by training independently and with different learning rates. The common representation acts as a implicit invariance objective to avoid the different spurious correlations captured by individual predictors. This in turn leads to better generalization performance. ModInv is tested on both the Reinforcement Learning and the Self-supervised Learning settings, showcasing strong performance boosts in both. It is extremely simple to implement, does not lead to any delay in walk clock times while training, and can be applied across different problem settings.
Generalized Fourier Features for Coordinate-Based Learning of Functions on Manifolds
Authors: Carlos Esteves, Tianjian Lu, Mohammed Suhail, Yi-fan Chen‎, Ameesh Makadia
Date: 2021-09-29 00:00:00

Recently, positional encoding of input coordinates has been found crucial to enable learning of high-frequency functions with multilayer perceptrons taking low-dimensional coordinate values.  In this setting, sinusoids are typically used as a basis for the encoding, which is commonly referred to as "Fourier Features".  However, using sinusoids as a basis assumes that the input coordinates lie on Euclidean space.  In this work, we generalize positional encoding with Fourier features to non-Euclidean manifolds.  We find appropriate bases for positional encoding on manifolds through generalizations of Fourier series.  By ensuring the encodings lie on a hypersphere and that the appropriate shifts on the manifold preserve inner-products between encodings, our model approximates convolutions on the manifold, according to the neural tangent kernel (NTK) assumptions. We demonstrate our method on various tasks on different manifolds: 1) learning panoramas on the sphere, 2) learning probability distributions on the rotation manifold, 3) learning neural radiance fields on the product of cube and sphere, and 4) learning light fields represented as the product of spheres.
Efficient Training and Inference of Hypergraph Reasoning Networks
Authors: Guangxuan Xiao, Leslie Pack Kaelbling, Jiajun Wu, Jiayuan Mao
Date: 2021-09-29 00:00:00
Tasks: Knowledge Graphs, Logical Reasoning, World Knowledge

We study the problem of hypergraph reasoning in large domains, e.g., predicting the relationship between several entities based on the input facts. We observe that in logical reasoning, logical rules (e.g., my parent's parent is my grandparent) usually apply locally (e.g., only three people are involved in a grandparent rule), and sparsely (e.g., the grandparent relationship is sparse across all pairs of people in the world). Inspired by these observations, we propose Sparse and Local Neural Logic Machines (SpaLoc), a structured neural network for hypergraph reasoning. To leverage the sparsity in hypergraph neural networks, SpaLoc represents the grounding of relationships such as parent and grandparent as sparse tensors and uses neural networks and finite-domain quantification operations to infer new facts based on the input. We further introduce a sparsification loss to regularize the number of hyperedges in intermediate layers of a SpaLoc model. To enable training on large-scale graphs such as real-world knowledge graphs, SpaLoc makes training and inference-time sub-sampling of the input graphs. To remedy the information loss in sampled sub-graphs, we propose a novel sampling and label calibration paradigm based on an information-theoretic measure information sufficiency. Our SpaLoc shows superior accuracy and efficiency on synthetic datasets compared with prior art and achieves state-of-the-art performance on several real-world knowledge graph reasoning benchmarks.
On the regularization landscape for the linear recommendation models
Authors: Dong Li, Zhenming Liu, Ruoming Jin, Zhi Liu, Jing Gao, Bin Ren
Date: 2021-09-29 00:00:00

Recently, a wide range of recommendation algorithms inspired by deep learning techniques have emerged as the performance leaders several standard recommendation benchmarks. While these algorithms were built on different DL techniques (e.g., dropouts, autoencoder), they have similar performance and even similar cost functions. This paper studies whether the models' comparable performance are sheer coincidence, or they can be unified into a single framework. We find that all linear performance leaders effectively add only a nuclear-norm based regularizer, or a Frobenius-norm based regularizer. The former ones possess a (surprisnig) rigid structure that limits the models' predictive power but their solutions are low rank and have closed form. The latter ones are more expressive and more efficient for recommendation but their solutions are either full-rank or require executing hard-to-tune numeric procedures such as ADMM. Along this line of finding, we further propose two low-rank, closed-form solutions, derived from carefully generalizing Frobenius-norm based regularizers. The new solutions get the best of both nuclear-norm and Frobenius-norm world.
Prototype memory and attention mechanisms for few shot image generation
Authors: Tianqin Li, Zijie Li, Andrew Luo, Harold Rockwell, Amir Barati Farimani, Tai Sing Lee
Date: 2021-09-29 00:00:00
Proceeding: ICLR 2022 4
Tasks: Image Generation, Online Clustering

Recent discoveries indicate that the neural codes in the primary visual cortex (V1) of macaque monkeys are complex, diverse and sparse. This leads us to ponder the computational advantages and functional role of these “grandmother cells." Here, we propose that such cells can serve as prototype memory priors that bias and shape the distributed feature processing within the image generation process in the brain. These memory prototypes are learned by momentum online clustering and are utilized via a memory-based attention operation, which we define as Memory Concept Attention (MoCA). To test our proposal, we show in a few-shot image generation task, that having a prototype memory during attention can improve image synthesis quality, learn interpretable visual concept clusters, as well as improve the robustness of the model. Interestingly, we also find that our attentional memory mechanism can implicitly modify the horizontal connections by updating the transformation into the prototype embedding space for self-attention. Insofar as GANs can be seen as plausible models for reasoning about the top-down synthesis in the analysis-by-synthesis loop of the hierarchical visual cortex, our findings demonstrate a plausible computational role for these “prototype concept" neurons in visual processing in the brain.
A Step-Wise Weighting Approach for Controllable Text Generation
Authors: Zichao Wang, Weili Nie, Zhenwei Dai, Richard Baraniuk
Date: 2021-09-29 00:00:00
Tasks: Attribute, Language Modeling, Language Modelling, Text Generation

We study the problem of controllable text generation (CTG): steering a language model (LM) to generate text with a desired attribute. Many existing approaches either require extensive training/fine-tuning of the LM for each single attribute under control or are slow to generate text. To this end, we first propose a framework based on step-wise energy-based models (EBMs) that is efficient in sampling and flexible in a wide range of practical CTG scenarios. Indeed, a number of existing CTG methods are special instances of our framework with a specific EBM design. In different control scenarios, we then design the respective energy functions that strategically up- or down-weigh the probabilities of keywords associated with a certain control attribute at each generation step. In experiments, we show that our simple and efficient approach is surprisingly competitive against more computationally expensive strong baselines, and even achieving new state-of-the-art performances in several cases. Our framework also provides a tuning hyper-parameter that nicely trades off generation quality and control satisfaction, enabling practitioners to easily adjust it to meet their needs.
How does Contrastive Pre-training Connect Disparate Domains?
Authors: Kendrick Shen, Robbie Matthew Jones, Ananya Kumar, Sang Michael Xie, Percy Liang
Date: 2021-09-29 00:00:00
Tasks: Contrastive Learning, Domain Adaptation, Unsupervised Domain Adaptation

Pre-training on massive unlabeled datasets greatly improves accuracy under distribution shifts. As a first step toward understanding this, we study a popular pre-training method, contrastive learning, in the unsupervised domain adaptation (UDA) setting where we only have labeled data from a source domain and unlabeled data from a target domain. We begin by showing on 4 benchmark datasets that out-of-the-box contrastive pre-training (even without large-scale unlabeled data) is competitive with other UDA methods. Intuitions from classical UDA methods such as domain adversarial training focus on bringing the domains together in feature space to improve generalization from source to target. Surprisingly, we find that contrastive pre-training learns features that are very far apart between the source and target domains. How then does contrastive learning improve robustness to distribution shift? We develop a conceptual model for contrastive learning under domain shifts, where data augmentations form connections between classes and domains that can be far apart. We propose a new measure of connectivity ---the relative connection strengths between same and different classes across domains---that governs the success of contrastive pre-training for domain adaptation in a simple example and strongly correlates with our results on benchmark datasets.
Resmax: An Alternative Soft-Greedy Operator for Reinforcement Learning
Authors: Erfan Miahi, Revan MacQueen, Alex Ayoub, Abbas Masoumzadeh, Martha White
Date: 2021-09-29 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Soft-greedy operators, namely $\varepsilon$-greedy and softmax, remain a common choice to induce a basic level of exploration for action-value methods in reinforcement learning. These operators, however, have a few critical limitations. In this work, we investigate a simple soft-greedy operator, which we call resmax, that takes actions proportionally to their suboptimality gap: the residual to the estimated maximal value. It is simple to use and ensures coverage of the state-space like $\varepsilon$-greedy, but focuses exploration more on potentially promising actions like softmax. Further, it does not concentrate probability as quickly as softmax, and so better avoids overemphasizing sub-optimal actions that appear high-valued during learning. Additionally, we prove it is a non-expansion for any fixed exploration hyperparameter, unlike the softmax policy which requires a state-action specific temperature to obtain a non-expansion (called mellowmax). We empirically validate that resmax is comparable to or outperforms $\varepsilon$-greedy and softmax across a variety of environments in tabular and deep RL.
An Information Fusion Approach to Learning with Instance-Dependent Label Noise
Authors: Zhimeng Jiang, Kaixiong Zhou, Zirui Liu, Li Li, Rui Chen, Soo-Hyun Choi, Xia Hu
Date: 2021-09-29 00:00:00
Proceeding: ICLR 2022 4

Instance-dependent label noise (IDN) widely exists in real-world datasets and usually misleads the training of deep neural networks. Noise transition matrix (NTM) (i.e., the probability that clean labels flip into noisy labels) is used to characterize the label noise and can be adopted to bridge the gap between clean and noisy underlying data distribution. However, most of instances are long-tail, i.e., the number of appearance for each instance is usually limited, which leads to the gap between underlying distribution and empirical distribution. Therefore, the genuine problem caused by IDN is \emph{empirical}, instead of underlying, \emph{data distribution mismatch} during training. To directly tackle the empirical distribution mismatch problem, we propose \emph{posterior transition matrix} (PTM) to posteriorly model label noise given limited observed noisy labels achieving \emph{statistically consistent classifiers}. Note that even if the instance is corrupted by the same NTM, the intrinsic randomness incurs to different noisy labels, and thus requires different correction methods. Motivated by this observation, we propose an \textbf{I}nformation \textbf{F}usion (IF) approach to fine-tune the NTM based on estimated PTM. Specifically, we adopt the noisy labels and model predicted probability to estimate PTM and then correct the NTM in \emph{forward propagation}. Empirical evaluations on synthetic and real-world datasets demonstrate that
our method is superior to the state-of-the-art approaches, and achieves more stable training for instance-dependent label noise.
Antonymy-Synonymy Discrimination through the Repelling Parasiamese Neural Network
Authors: Mathias Etcheverry, Dina Wonsever
Date: 2021-09-29 00:00:00
Tasks: Word Embeddings

Antonymic and synonymic pairs may both occur nearby in word embeddings spaces because they have similar distributional information. Different methods have been used in order to distinguish antonyms from synonyms, making the antonymy-synonymy discrimination a popular NLP task. In this work, we propose the repelling parasiamese neural network, a model which considers a siamese network for synonymy and a parasiamese network for antonymy, both sharing the same base network. Relying in the antagonism between synoymy and antonymy, the model attempts to repell siamese and parasiamese outputs making use of the contrastive loss functions. We experimentally show that the repelling parasiamese network achieves state-of-the-art results on this task.
Decentralized Cross-Entropy Method for Model-Based Reinforcement Learning
Authors: Zichen Zhang, Jun Jin, Martin Jagersand, Jun Luo, Dale Schuurmans
Date: 2021-09-29 00:00:00
Tasks: continuous-control, Continuous Control, Decision Making, Model-based Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Sequential Decision Making

Cross-Entropy Method (CEM) is a popular approach to planning in model-based reinforcement learning.
It has so far always taken a \textit{centralized} approach where the sampling distribution is updated \textit{centrally} based on the result of a top-$k$ operation applied to \textit{all samples}.
We show that such a \textit{centralized} approach makes CEM vulnerable to local optima and impair its sample efficiency, even in a one-dimensional multi-modal optimization task.
In this paper, we propose \textbf{Decent}ralized \textbf{CEM (DecentCEM)} where an ensemble of CEM instances run independently from one another and each performs a local improvement of its own sampling distribution.
In the exemplar optimization task, the proposed decentralized approach DecentCEM finds the global optimum much more consistently than the existing CEM approaches that use either a single Gaussian distribution or a mixture of Gaussians.
Further, we extend the decentralized approach to sequential decision-making problems where we show in 13 continuous control benchmark environments that it matches or outperforms the state-of-the-art CEM algorithms in most cases, under the same budget of the total number of samples for planning.
Tessellated 2D Convolution Networks: A Robust Defence against Adversarial Attacks
Authors: Swarnava Das, Pabitra Mitra, Debasis Ganguly
Date: 2021-09-29 00:00:00
Tasks: image-classification, Image Classification

Data-driven (deep) learning approaches for image classification are prone to adversarial attacks. This means that an adversarial crafted image which is sufficiently close (visually indistinguishable) to its representative class can often be misclassified to be a member of a different class. A reason why deep neural approaches exhibits such vulnerability towards adversarial threats is mainly because the abstract representations learned in a data-driven manner often do not correlate well with human perceived features. To mitigate this problem, we propose the tessellated 2d convolution network, a novel divide-and-conquer based approach, which first independently learns the abstract representations of non-overlapping regions within an image, and then learns how to combine these representations to infer its class. It turns out that a non-uniform tiling of an image which ensures that the difference between the maximum and the minimum region sizes is not too large is the most robust way to construct such a tessellated 2d convolution network. This criterion can be achieved, among other schemes, by using a Mondrian tessellation of the input image. Our experiments demonstrate that our tessellated networks provides a more robust defence mechanism against gradient-based adversarial attacks in comparison to conventional deep neural models.
Practical Adversarial Attacks on Brain--Computer Interfaces
Authors: Rodolfo Octavio Siller Quintanilla, Xiaying Wang, Michael Hersche, Luca Benini, Gagandeep Singh
Date: 2021-09-29 00:00:00
Tasks: EEG, Electroencephalogram (EEG)

Deep learning has been widely employed in brain--computer interfaces (BCIs) to decode a subject's intentions based on recorded brain activities enabling direct interaction with computers and machines. BCI systems play a crucial role in motor rehabilitation and have recently experienced a significant market boost as consumer-grade products. Recent studies have shown that deep learning-based BCIs are vulnerable to adversarial attacks. Failures in such systems might cause medical misdiagnoses, physical harm, and financial damages, hence it is of utmost importance to analyze and understand in-depth, potential malicious attacks to develop countermeasures. In this work, we present the first study that analyzes and models adversarial attacks based on physical domain constraints in EEG-based BCIs. Specifically, we assess the robustness of EEGNet which is the current state-of-the-art network for embedded BCIs. We propose new methods to induce denial-of-service attacks and incorporate domain-specific insights and constraints to accomplish two key goals: (i) create smooth adversarial attacks that are physiologically plausible; (ii) consider the realistic case where the attack happens at the origin of the signal acquisition and it propagates on the human head. Our results show that EEGNet is significantly vulnerable to adversarial attacks with an attack success rate of more than 50\%. With our work, we want to raise awareness and incentivize future developments of proper countermeasures.
The Remarkable Effectiveness of Combining Policy and Value Networks in A*-based Deep RL for AI Planning
Authors: Dieqiao Feng, Carla P Gomes, Bart Selman
Date: 2021-09-29 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning (RL)

Despite the tremendous success of applying traditional backtrack-style combinatorial search methods in various NP-complete domains such as SAT and CSP as well as using deep reinforcement learning (RL) to tackle two-player games such as Go, PSPACE-hard AI planning has remained out of reach for current AI planning systems. Even carefully designed domain-specific solvers fail quickly due to the exponential combinatorial search space on hard instances. Recent work based on deep learning guided search algorithms that combine traditional search-based methods, such as A\textsc{*} and MCTS search, with deep neural networks' heuristic prediction has shown promising progress. These methods can solve a significant number of hard planning instances beyond  specialized solvers. To better understanding why these approaches work we study the interplay of the policy and value networks in A\textsc{*}-based deep RL and show the surprising effectiveness of the policy network, further enhanced by the value network, as a guiding heuristic for A\textsc{*}. To further understand the phenomena, we study the cost distributions of deep planners and found  planning instances can have heavy-tailed runtime distributions, with tails both on the right-hand and left-hand sides. In particular, for the first time, we show the existence of {\textit{left}} heavy tails and propose a theoretical model that can explain the appearance of these tails. We provide extensive  experimental data supporting our model. The experiments show the critical role of the policy network as a powerful heuristic guiding  A\textsc{*}, which can lead to  left tails with polynomial scaling by avoiding exploring exponential size sub-trees early on in the search. Our results also demonstrate the importance of random restart strategies, as are widely used in traditional combinatorial solvers, for deep reinforcement learning and deep AI planning systems to avoid left and right heavy tails.