pwc-archive/papers-with-abstracts huggingface dataset:
PreprintResolver: Improving Citation Quality by Resolving Published Versions of ArXiv Preprints using Literature Databases
Authors: Louise Bloch, Johannes Rückert, Christoph M. Friedrich
Date: 2023-09-04 00:00:00

The growing impact of preprint servers enables the rapid sharing of time-sensitive research. Likewise, it is becoming increasingly difficult to distinguish high-quality, peer-reviewed research from preprints. Although preprints are often later published in peer-reviewed journals, this information is often missing from preprint servers. To overcome this problem, the PreprintResolver was developed, which uses four literature databases (DBLP, SemanticScholar, OpenAlex, and CrossRef / CrossCite) to identify preprint-publication pairs for the arXiv preprint server. The target audience focuses on, but is not limited to inexperienced researchers and students, especially from the field of computer science. The tool is based on a fuzzy matching of author surnames, titles, and DOIs. Experiments were performed on a sample of 1,000 arXiv-preprints from the research field of computer science and without any publication information. With 77.94 %, computer science is highly affected by missing publication information in arXiv. The results show that the PreprintResolver was able to resolve 603 out of 1,000 (60.3 %) arXiv-preprints from the research field of computer science and without any publication information. All four literature databases contributed to the final result. In a manual validation, a random sample of 100 resolved preprints was checked. For all preprints, at least one result is plausible. For nine preprints, more than one result was identified, three of which are partially invalid. In conclusion the PreprintResolver is suitable for individual, manually reviewed requests, but less suitable for bulk requests. The PreprintResolver tool (https://preprintresolver.eu, Available from 2023-08-01) and source code (https://gitlab.com/ippolis_wp3/preprint-resolver, Accessed: 2023-07-19) is available online.
Distributional Domain-Invariant Preference Matching for Cross-Domain Recommendation
Authors: Jing Du, Zesheng Ye, Bin Guo, Zhiwen Yu, Lina Yao
Date: 2023-09-04 00:00:00
Tasks: Preference Mapping

Learning accurate cross-domain preference mappings in the absence of overlapped users/items has presented a persistent challenge in Non-overlapping Cross-domain Recommendation (NOCDR). Despite the efforts made in previous studies to address NOCDR, several limitations still exist. Specifically, 1) while some approaches substitute overlapping users/items with overlapping behaviors, they cannot handle NOCDR scenarios where such auxiliary information is unavailable; 2) often, cross-domain preference mapping is modeled by learning deterministic explicit representation matchings between sampled users in two domains. However, this can be biased due to individual preferences and thus fails to incorporate preference continuity and universality of the general population. In light of this, we assume that despite the scattered nature of user behaviors, there exists a consistent latent preference distribution shared among common people. Modeling such distributions further allows us to capture the continuity in user behaviors within each domain and discover preference invariance across domains. To this end, we propose a Distributional domain-invariant Preference Matching method for non-overlapping Cross-Domain Recommendation (DPMCDR). For each domain, we hierarchically approximate a posterior of domain-level preference distribution with empirical evidence derived from user-item interactions. Next, we aim to build distributional implicit matchings between the domain-level preferences of two domains. This process involves mapping them to a shared latent space and seeking a consensus on domain-invariant preference by minimizing the distance between their distributional representations therein. In this way, we can identify the alignment of two non-overlapping domains if they exhibit similar patterns of domain-invariant preference.
In-processing User Constrained Dominant Sets for User-Oriented Fairness in Recommender Systems
Authors: Zhongxuan Han, Chaochao Chen, Xiaolin Zheng, Weiming Liu, Jun Wang, Wenjie Cheng, Yuyuan Li
Date: 2023-09-04 00:00:00
Tasks: Fairness, Recommendation Systems

Recommender systems are typically biased toward a small group of users, leading to severe unfairness in recommendation performance, i.e., User-Oriented Fairness (UOF) issue. The existing research on UOF is limited and fails to deal with the root cause of the UOF issue: the learning process between advantaged and disadvantaged users is unfair. To tackle this issue, we propose an In-processing User Constrained Dominant Sets (In-UCDS) framework, which is a general framework that can be applied to any backbone recommendation model to achieve user-oriented fairness. We split In-UCDS into two stages, i.e., the UCDS modeling stage and the in-processing training stage. In the UCDS modeling stage, for each disadvantaged user, we extract a constrained dominant set (a user cluster) containing some advantaged users that are similar to it. In the in-processing training stage, we move the representations of disadvantaged users closer to their corresponding cluster by calculating a fairness loss. By combining the fairness loss with the original backbone model loss, we address the UOF issue and maintain the overall recommendation performance simultaneously. Comprehensive experiments on three real-world datasets demonstrate that In-UCDS outperforms the state-of-the-art methods, leading to a fairer model with better overall recommendation performance.
Multi-Relational Contrastive Learning for Recommendation
Authors: Wei Wei, Lianghao Xia, Chao Huang
Date: 2023-09-03 00:00:00
Tasks: Contrastive Learning, Recommendation Systems, Relation

Personalized recommender systems play a crucial role in capturing users' evolving preferences over time to provide accurate and effective recommendations on various online platforms. However, many recommendation models rely on a single type of behavior learning, which limits their ability to represent the complex relationships between users and items in real-life scenarios. In such situations, users interact with items in multiple ways, including clicking, tagging as favorite, reviewing, and purchasing. To address this issue, we propose the Relation-aware Contrastive Learning (RCL) framework, which effectively models dynamic interaction heterogeneity. The RCL model incorporates a multi-relational graph encoder that captures short-term preference heterogeneity while preserving the dedicated relation semantics for different types of user-item interactions. Moreover, we design a dynamic cross-relational memory network that enables the RCL model to capture users' long-term multi-behavior preferences and the underlying evolving cross-type behavior dependencies over time. To obtain robust and informative user representations with both commonality and diversity across multi-behavior interactions, we introduce a multi-relational contrastive learning paradigm with heterogeneous short- and long-term interest modeling. Our extensive experimental studies on several real-world datasets demonstrate the superiority of the RCL recommender system over various state-of-the-art baselines in terms of recommendation accuracy and effectiveness.
Hessian-aware Quantized Node Embeddings for Recommendation
Authors: Huiyuan Chen, Kaixiong Zhou, Kwei-Herng Lai, Chin-Chia Michael Yeh, Yan Zheng, Xia Hu, Hao Yang
Date: 2023-09-02 00:00:00
Tasks: Recommendation Systems, Retrieval

Graph Neural Networks (GNNs) have achieved state-of-the-art performance in recommender systems. Nevertheless, the process of searching and ranking from a large item corpus usually requires high latency, which limits the widespread deployment of GNNs in industry-scale applications. To address this issue, many methods compress user/item representations into the binary embedding space to reduce space requirements and accelerate inference. Also, they use the Straight-through Estimator (STE) to prevent vanishing gradients during back-propagation. However, the STE often causes the gradient mismatch problem, leading to sub-optimal results. In this work, we present the Hessian-aware Quantized GNN (HQ-GNN) as an effective solution for discrete representations of users/items that enable fast retrieval. HQ-GNN is composed of two components: a GNN encoder for learning continuous node embeddings and a quantized module for compressing full-precision embeddings into low-bit ones. Consequently, HQ-GNN benefits from both lower memory requirements and faster inference speeds compared to vanilla GNNs. To address the gradient mismatch problem in STE, we further consider the quantized errors and its second-order derivatives for better stability. The experimental results on several large-scale datasets show that HQ-GNN achieves a good balance between latency and performance.
Pure Message Passing Can Estimate Common Neighbor for Link Prediction
Authors: Kaiwen Dong, Zhichun Guo, Nitesh V. Chawla
Date: 2023-09-02 00:00:00
Tasks: Graph Representation Learning, Link Prediction, Prediction, Representation Learning

Message Passing Neural Networks (MPNNs) have emerged as the {\em de facto} standard in graph representation learning. However, when it comes to link prediction, they often struggle, surpassed by simple heuristics such as Common Neighbor (CN). This discrepancy stems from a fundamental limitation: while MPNNs excel in node-level representation, they stumble with encoding the joint structural features essential to link prediction, like CN. To bridge this gap, we posit that, by harnessing the orthogonality of input vectors, pure message-passing can indeed capture joint structural features. Specifically, we study the proficiency of MPNNs in approximating CN heuristics. Based on our findings, we introduce the Message Passing Link Predictor (MPLP), a novel link prediction model. MPLP taps into quasi-orthogonal vectors to estimate link-level structural features, all while preserving the node-level complexities. Moreover, our approach demonstrates that leveraging message-passing to capture structural features could offset MPNNs' expressiveness limitations at the expense of estimation variance. We conduct experiments on benchmark datasets from various domains, where our method consistently outperforms the baseline methods.
A Post-Training Approach for Mitigating Overfitting in Quantum Convolutional Neural Networks
Authors: Aakash Ravindra Shinde, Charu Jain, Amir Kalev
Date: 2023-09-04 00:00:00

Quantum convolutional neural network (QCNN), an early application for quantum computers in the NISQ era, has been consistently proven successful as a machine learning (ML) algorithm for several tasks with significant accuracy. Derived from its classical counterpart, QCNN is prone to overfitting. Overfitting is a typical shortcoming of ML models that are trained too closely to the availed training dataset and perform relatively poorly on unseen datasets for a similar problem. In this work we study post-training approaches for mitigating overfitting in QCNNs. We find that a straightforward adaptation of a classical post-training method, known as neuron dropout, to the quantum setting leads to a significant and undesirable consequence: a substantial decrease in success probability of the QCNN. We argue that this effect exposes the crucial role of entanglement in QCNNs and the vulnerability of QCNNs to entanglement loss. Hence, we propose a parameter adaptation method as an alternative method. Our method is computationally efficient and is found to successfully handle overfitting in the test cases.
Nonlinear Granger Causality using Kernel Ridge Regression
Authors: Wojciech "Victor" Fulmyk
Date: 2023-09-10 00:00:00
Tasks: regression

I introduce a novel algorithm and accompanying Python library, named mlcausality, designed for the identification of nonlinear Granger causal relationships. This novel algorithm uses a flexible plug-in architecture that enables researchers to employ any nonlinear regressor as the base prediction model. Subsequently, I conduct a comprehensive performance analysis of mlcausality when the prediction regressor is the kernel ridge regressor with the radial basis function kernel. The results demonstrate that mlcausality employing kernel ridge regression achieves competitive AUC scores across a diverse set of simulated data. Furthermore, mlcausality with kernel ridge regression yields more finely calibrated $p$-values in comparison to rival algorithms. This enhancement enables mlcausality to attain superior accuracy scores when using intuitive $p$-value-based thresholding criteria. Finally, mlcausality with the kernel ridge regression exhibits significantly reduced computation times compared to existing nonlinear Granger causality algorithms. In fact, in numerous instances, this innovative approach achieves superior solutions within computational timeframes that are an order of magnitude shorter than those required by competing algorithms.
Efficient Emotional Adaptation for Audio-Driven Talking-Head Generation
Authors: Yuan Gan, Zongxin Yang, Xihang Yue, Lingyun Sun, Yi Yang
Date: 2023-09-10 00:00:00
Proceeding: ICCV 2023 1
Tasks: Talking Head Generation

Audio-driven talking-head synthesis is a popular research topic for virtual human-related applications. However, the inflexibility and inefficiency of existing methods, which necessitate expensive end-to-end training to transfer emotions from guidance videos to talking-head predictions, are significant limitations. In this work, we propose the Emotional Adaptation for Audio-driven Talking-head (EAT) method, which transforms emotion-agnostic talking-head models into emotion-controllable ones in a cost-effective and efficient manner through parameter-efficient adaptations. Our approach utilizes a pretrained emotion-agnostic talking-head transformer and introduces three lightweight adaptations (the Deep Emotional Prompts, Emotional Deformation Network, and Emotional Adaptation Module) from different perspectives to enable precise and realistic emotion controls. Our experiments demonstrate that our approach achieves state-of-the-art performance on widely-used benchmarks, including LRW and MEAD. Additionally, our parameter-efficient adaptations exhibit remarkable generalization ability, even in scenarios where emotional training videos are scarce or nonexistent. Project website: https://yuangan.github.io/eat/
PlumberNet: Fixing interference leakage after GEV beamforming
Authors: François Grondin, Caleb Rascón
Date: 2023-09-10 00:00:00
Tasks: Speech Enhancement

Spatial filters can exploit deep-learning-based speech enhancement models to increase their reliability in scenarios with multiple speech sources scenarios. To further improve speech quality, it is common to perform postfiltering on the estimated target speech obtained with spatial filtering. In this work, Generalized Eigenvalue (GEV) beamforming is employed to provide the leakage estimation, along with the estimation of the target speech, to be later used for postfiltering. This improves the enhancement performance over a postfilter that uses the target speech and a reference microphone signal. This work also demonstrates that the spatial covariance matrices (SCMs) can be accurately estimated from the direction of arrival (DoA) of the target and a discriminative selection amongst the pairwise estimated time-frequency masks.
Adaptive conformal classification with noisy labels
Authors: Matteo Sesia, Y. X. Rachel Wang, Xin Tong
Date: 2023-09-10 00:00:00
Tasks: Classification, Conformal Prediction

This paper develops novel conformal prediction methods for classification tasks that can automatically adapt to random label contamination in the calibration sample, leading to more informative prediction sets with stronger coverage guarantees compared to state-of-the-art approaches. This is made possible by a precise characterization of the effective coverage inflation (or deflation) suffered by standard conformal inferences in the presence of label contamination, which is then made actionable through new calibration algorithms. Our solution is flexible and can leverage different modeling assumptions about the label contamination process, while requiring no knowledge of the underlying data distribution or of the inner workings of the machine-learning classifier. The advantages of the proposed methods are demonstrated through extensive simulations and an application to object classification with the CIFAR-10H image data set.
Robot Parkour Learning
Authors: Ziwen Zhuang, Zipeng Fu, Jianren Wang, Christopher Atkeson, Soeren Schwertfeger, Chelsea Finn, Hang Zhao
Date: 2023-09-11 00:00:00

Parkour is a grand challenge for legged locomotion that requires robots to overcome various obstacles rapidly in complex environments. Existing methods can generate either diverse but blind locomotion skills or vision-based but specialized skills by using reference animal data or complex rewards. However, autonomous parkour requires robots to learn generalizable skills that are both vision-based and diverse to perceive and react to various scenarios. In this work, we propose a system for learning a single end-to-end vision-based parkour policy of diverse parkour skills using a simple reward without any reference motion data. We develop a reinforcement learning method inspired by direct collocation to generate parkour skills, including climbing over high obstacles, leaping over large gaps, crawling beneath low barriers, squeezing through thin slits, and running. We distill these skills into a single vision-based parkour policy and transfer it to a quadrupedal robot using its egocentric depth camera. We demonstrate that our system can empower two different low-cost robots to autonomously select and execute appropriate parkour skills to traverse challenging real-world environments.
Hypothesis Search: Inductive Reasoning with Language Models
Authors: Ruocheng Wang, Eric Zelikman, Gabriel Poesia, Yewen Pu, Nick Haber, Noah D. Goodman
Date: 2023-09-11 00:00:00
Tasks: ARC, In-Context Learning

Inductive reasoning is a core problem-solving capacity: humans can identify underlying principles from a few examples, which robustly generalize to novel scenarios. Recent work evaluates large language models (LLMs) on inductive reasoning tasks by directly prompting them yielding "in context learning." This works well for straightforward inductive tasks but performs poorly on complex tasks such as the Abstraction and Reasoning Corpus (ARC). In this work, we propose to improve the inductive reasoning ability of LLMs by generating explicit hypotheses at multiple levels of abstraction: we prompt the LLM to propose multiple abstract hypotheses about the problem, in natural language, then implement the natural language hypotheses as concrete Python programs. These programs can be verified by running on observed examples and generalized to novel inputs. To reduce the hypothesis search space, we explore steps to filter the set of hypotheses to implement: we either ask the LLM to summarize them into a smaller set of hypotheses or ask human annotators to select a subset. We verify our pipeline's effectiveness on the ARC visual inductive reasoning benchmark, its variant 1D-ARC, string transformation dataset SyGuS, and list transformation dataset List Functions. On a random 100-problem subset of ARC, our automated pipeline using LLM summaries achieves 30% accuracy, outperforming the direct prompting baseline (accuracy of 17%). With the minimal human input of selecting from LLM-generated candidates, performance is boosted to 33%. Our ablations show that both abstract hypothesis generation and concrete program representations benefit LLMs on inductive reasoning tasks.
Errors are Robustly Tamed in Cumulative Knowledge Processes
Authors: Anna Brandenberger, Cassandra Marcussen, Elchanan Mossel, Madhu Sudan
Date: 2023-09-11 00:00:00

We study processes of societal knowledge accumulation, where the validity of a new unit of knowledge depends both on the correctness of its derivation and on the validity of the units it depends on. A fundamental question in this setting is: If a constant fraction of the new derivations is wrong, can investing a constant fraction, bounded away from one, of effort ensure that a constant fraction of knowledge in society is valid? Ben-Eliezer, Mikulincer, Mossel, and Sudan (ITCS 2023) introduced a concrete probabilistic model to analyze such questions and showed an affirmative answer to this question. Their study, however, focuses on the simple case where each new unit depends on just one existing unit, and units attach according to a $\textit{preferential attachment rule}$. In this work, we consider much more general families of cumulative knowledge processes, where new units may attach according to varied attachment mechanisms and depend on multiple existing units. We also allow a (random) fraction of insertions of adversarial nodes. We give a robust affirmative answer to the above question by showing that for $\textit{all}$ of these models, as long as many of the units follow simple heuristics for checking a bounded number of units they depend on, all errors will be eventually eliminated. Our results indicate that preserving the quality of large interdependent collections of units of knowledge is feasible, as long as careful but not too costly checks are performed when new units are derived/deposited.
Memory Injections: Correcting Multi-Hop Reasoning Failures during Inference in Transformer-Based Language Models
Authors: Mansi Sakarvadia, Aswathy Ajith, Arham Khan, Daniel Grzenda, Nathaniel Hudson, André Bauer, Kyle Chard, Ian Foster
Date: 2023-09-11 00:00:00

Answering multi-hop reasoning questions requires retrieving and synthesizing information from diverse sources. Large Language Models (LLMs) struggle to perform such reasoning consistently. Here we propose an approach to pinpoint and rectify multi-hop reasoning failures through targeted memory injections on LLM attention heads. First, we analyze the per-layer activations of GPT-2 models in response to single and multi-hop prompts. We then propose a mechanism that allows users to inject pertinent prompt-specific information, which we refer to as "memories," at critical LLM locations during inference. By thus enabling the LLM to incorporate additional relevant information during inference, we enhance the quality of multi-hop prompt completions. We show empirically that a simple, efficient, and targeted memory injection into a key attention layer can often increase the probability of the desired next token in multi-hop tasks, by up to 424%.
Temporal Action Localization with Enhanced Instant Discriminability
Authors: Dingfeng Shi, Qiong Cao, Yujie Zhong, Shan An, Jian Cheng, Haogang Zhu, DaCheng Tao
Date: 2023-09-11 00:00:00
Tasks: Action Detection, Action Localization, Temporal Action Localization

Temporal action detection (TAD) aims to detect all action boundaries and their corresponding categories in an untrimmed video. The unclear boundaries of actions in videos often result in imprecise predictions of action boundaries by existing methods. To resolve this issue, we propose a one-stage framework named TriDet. First, we propose a Trident-head to model the action boundary via an estimated relative probability distribution around the boundary. Then, we analyze the rank-loss problem (i.e. instant discriminability deterioration) in transformer-based methods and propose an efficient scalable-granularity perception (SGP) layer to mitigate this issue. To further push the limit of instant discriminability in the video backbone, we leverage the strong representation capability of pretrained large models and investigate their performance on TAD. Last, considering the adequate spatial-temporal context for classification, we design a decoupled feature pyramid network with separate feature pyramids to incorporate rich spatial context from the large model for localization. Experimental results demonstrate the robustness of TriDet and its state-of-the-art performance on multiple TAD datasets, including hierarchical (multilabel) TAD datasets.