pwc-archive/papers-with-abstracts huggingface dataset:
Noise-Robust Dense Retrieval via Contrastive Alignment Post Training
Authors: Daniel Campos, ChengXiang Zhai, Alessandro Magnani
Date: 2023-04-06 00:00:00
Tasks: Data Augmentation, Document Ranking, Information Retrieval, Natural Questions, Passage Retrieval, Retrieval

The success of contextual word representations and advances in neural information retrieval have made dense vector-based retrieval a standard approach for passage and document ranking. While effective and efficient, dual-encoders are brittle to variations in query distributions and noisy queries. Data augmentation can make models more robust but introduces overhead to training set generation and requires retraining and index regeneration. We present Contrastive Alignment POst Training (CAPOT), a highly efficient finetuning method that improves model robustness without requiring index regeneration, the training set optimization, or alteration. CAPOT enables robust retrieval by freezing the document encoder while the query encoder learns to align noisy queries with their unaltered root. We evaluate CAPOT noisy variants of MSMARCO, Natural Questions, and Trivia QA passage retrieval, finding CAPOT has a similar impact as data augmentation with none of its overhead.
Personalising Digital Health Behaviour Change Interventions using Machine Learning and Domain Knowledge
Authors: Aneta Lisowska, Szymon Wilk, Mor Peleg
Date: 2023-04-06 00:00:00
Tasks: counterfactual

We are developing a virtual coaching system that helps patients adhere to behavior change interventions (BCI). Our proposed system predicts whether a patient will perform the targeted behaviour and uses counterfactual examples with feature control to guide personalisation of BCI. We use simulated patient data with varying levels of receptivity to intervention to arrive at the study design which would enable evaluation of our system.
Handling Wikidata Qualifiers in Reasoning
Authors: Sahar Aljalbout, Gilles Falquet, Didier Buchs
Date: 2023-04-06 00:00:00

Wikidata is a knowledge graph increasingly adopted by many communities for diverse applications. Wikidata statements are annotated with qualifier-value pairs that are used to depict information, such as the validity context of the statement, its causality, provenances, etc. Handling the qualifiers in reasoning is a challenging problem. When defining inference rules (in particular, rules on ontological properties (x subclass of y, z instance of x, etc.)), one must consider the qualifiers, as most of them participate in the semantics of the statements. This poses a complex problem because a) there is a massive number of qualifiers, and b) the qualifiers of the inferred statement are often a combination of the qualifiers in the rule condition. In this work, we propose to address this problem by a) defining a categorization of the qualifiers b) formalizing the Wikidata model with a many-sorted logical language; the sorts of this language are the qualifier categories. We couple this logic with an algebraic specification that provides a means for effectively handling qualifiers in inference rules. Using Wikidata ontological properties, we show how to use the MSL and specification to reason on qualifiers. Finally, we discuss the methodology for practically implementing the work and present a prototype implementation. The work can be naturally extended, thanks to the extensibility of the many-sorted algebraic specification, to cover more qualifiers in the specification, such as uncertain time, recurring events, geographic locations, and others.
Decision-Focused Model-based Reinforcement Learning for Reward Transfer
Authors: Abhishek Sharma, Sonali Parbhoo, Omer Gottesman, Finale Doshi-Velez
Date: 2023-04-06 00:00:00
Tasks: Decision Making, Model-based Reinforcement Learning, reinforcement-learning, Reinforcement Learning

Model-based reinforcement learning (MBRL) provides a way to learn a transition model of the environment, which can then be used to plan personalized policies for different patient cohorts and to understand the dynamics involved in the decision-making process. However, standard MBRL algorithms are either sensitive to changes in the reward function or achieve suboptimal performance on the task when the transition model is restricted. Motivated by the need to use simple and interpretable models in critical domains such as healthcare, we propose a novel robust decision-focused (RDF) algorithm that learns a transition model that achieves high returns while being robust to changes in the reward function. We demonstrate our RDF algorithm can be used with several model classes and planning algorithms. We also provide theoretical and empirical evidence, on a variety of simulators and real patient data, that RDF can learn simple yet effective models that can be used to plan personalized policies.
Graph Collaborative Signals Denoising and Augmentation for Recommendation
Authors: Ziwei Fan, Ke Xu, Zhang Dong, Hao Peng, Jiawei Zhang, Philip S. Yu
Date: 2023-04-06 00:00:00
Tasks: Collaborative Filtering, Denoising, Recommendation Systems

Graph collaborative filtering (GCF) is a popular technique for capturing high-order collaborative signals in recommendation systems. However, GCF's bipartite adjacency matrix, which defines the neighbors being aggregated based on user-item interactions, can be noisy for users/items with abundant interactions and insufficient for users/items with scarce interactions. Additionally, the adjacency matrix ignores user-user and item-item correlations, which can limit the scope of beneficial neighbors being aggregated. In this work, we propose a new graph adjacency matrix that incorporates user-user and item-item correlations, as well as a properly designed user-item interaction matrix that balances the number of interactions across all users. To achieve this, we pre-train a graph-based recommendation method to obtain users/items embeddings, and then enhance the user-item interaction matrix via top-K sampling. We also augment the symmetric user-user and item-item correlation components to the adjacency matrix. Our experiments demonstrate that the enhanced user-item interaction matrix with improved neighbors and lower density leads to significant benefits in graph-based recommendation. Moreover, we show that the inclusion of user-user and item-item correlations can improve recommendations for users with both abundant and insufficient interactions. The code is in \url{https://github.com/zfan20/GraphDA}.
Maximal Ordinal Two-Factorizations
Authors: Dominik Dürrschnabel, Gerd Stumme
Date: 2023-04-06 00:00:00
Tasks: Relation, Vocal Bursts Valence Prediction

Given a formal context, an ordinal factor is a subset of its incidence relation that forms a chain in the concept lattice, i.e., a part of the dataset that corresponds to a linear order. To visualize the data in a formal context, Ganter and Glodeanu proposed a biplot based on two ordinal factors. For the biplot to be useful, it is important that these factors comprise as much data points as possible, i.e., that they cover a large part of the incidence relation. In this work, we investigate such ordinal two-factorizations. First, we investigate for formal contexts that omit ordinal two-factorizations the disjointness of the two factors. Then, we show that deciding on the existence of two-factorizations of a given size is an NP-complete problem which makes computing maximal factorizations computationally expensive. Finally, we provide the algorithm Ord2Factor that allows us to compute large ordinal two-factorizations.
Explainable AI And Visual Reasoning: Insights From Radiology
Authors: Robert Kaufman, David Kirsh
Date: 2023-04-06 00:00:00
Tasks: Diagnostic, Explainable Artificial Intelligence (XAI), Visual Reasoning

Why do explainable AI (XAI) explanations in radiology, despite their promise of transparency, still fail to gain human trust? Current XAI approaches provide justification for predictions, however, these do not meet practitioners' needs. These XAI explanations lack intuitive coverage of the evidentiary basis for a given classification, posing a significant barrier to adoption. We posit that XAI explanations that mirror human processes of reasoning and justification with evidence may be more useful and trustworthy than traditional visual explanations like heat maps. Using a radiology case study, we demonstrate how radiology practitioners get other practitioners to see a diagnostic conclusion's validity. Machine-learned classifications lack this evidentiary grounding and consequently fail to elicit trust and adoption by potential users. Insights from this study may generalize to guiding principles for human-centered explanation design based on human reasoning and justification of evidence.
HumanLight: Incentivizing Ridesharing via Human-centric Deep Reinforcement Learning in Traffic Signal Control
Authors: Dimitris M. Vlachogiannis, Hua Wei, Scott Moura, Jane Macfarlane
Date: 2023-04-05 00:00:00
Tasks: Deep Reinforcement Learning, Traffic Signal Control

Single occupancy vehicles are the most attractive transportation alternative for many commuters, leading to increased traffic congestion and air pollution. Advancements in information technologies create opportunities for smart solutions that incentivize ridesharing and mode shift to higher occupancy vehicles (HOVs) to achieve the car lighter vision of cities. In this study, we present HumanLight, a novel decentralized adaptive traffic signal control algorithm designed to optimize people throughput at intersections. Our proposed controller is founded on reinforcement learning with the reward function embedding the transportation-inspired concept of pressure at the person-level. By rewarding HOV commuters with travel time savings for their efforts to merge into a single ride, HumanLight achieves equitable allocation of green times. Apart from adopting FRAP, a state-of-the-art (SOTA) base model, HumanLight introduces the concept of active vehicles, loosely defined as vehicles in proximity to the intersection within the action interval window. The proposed algorithm showcases significant headroom and scalability in different network configurations considering multimodal vehicle splits at various scenarios of HOV adoption. Improvements in person delays and queues range from 15% to over 55% compared to vehicle-level SOTA controllers. We quantify the impact of incorporating active vehicles in the formulation of our RL model for different network structures. HumanLight also enables regulation of the aggressiveness of the HOV prioritization. The impact of parameter setting on the generated phase profile is investigated as a key component of acyclic signal controllers affecting pedestrian waiting times. HumanLight's scalable, decentralized design can reshape the resolution of traffic management to be more human-centric and empower policies that incentivize ridesharing and public transit systems.
Adaptive Feature Fusion: Enhancing Generalization in Deep Learning Models
Authors: Neelesh Mungoli
Date: 2023-04-04 00:00:00
Tasks: Deep Learning, speech-recognition, Speech Recognition

In recent years, deep learning models have demonstrated remarkable success in various domains, such as computer vision, natural language processing, and speech recognition. However, the generalization capabilities of these models can be negatively impacted by the limitations of their feature fusion techniques. This paper introduces an innovative approach, Adaptive Feature Fusion (AFF), to enhance the generalization of deep learning models by dynamically adapting the fusion process of feature representations. The proposed AFF framework is designed to incorporate fusion layers into existing deep learning architectures, enabling seamless integration and improved performance. By leveraging a combination of data-driven and model-based fusion strategies, AFF is able to adaptively fuse features based on the underlying data characteristics and model requirements. This paper presents a detailed description of the AFF framework, including the design and implementation of fusion layers for various architectures. Extensive experiments are conducted on multiple benchmark datasets, with the results demonstrating the superiority of the AFF approach in comparison to traditional feature fusion techniques. The analysis showcases the effectiveness of AFF in enhancing generalization capabilities, leading to improved performance across different tasks and applications. Finally, the paper discusses various real-world use cases where AFF can be employed, providing insights into its practical applicability. The conclusion highlights the potential for future research directions, including the exploration of advanced fusion strategies and the extension of AFF to other machine learning paradigms.
Online Learning for Scheduling MIP Heuristics
Authors: Antonia Chmiela, Ambros Gleixner, Pawel Lichocki, Sebastian Pokutta
Date: 2023-04-04 00:00:00
Tasks: Scheduling

Mixed Integer Programming (MIP) is NP-hard, and yet modern solvers often solve large real-world problems within minutes. This success can partially be attributed to heuristics. Since their behavior is highly instance-dependent, relying on hard-coded rules derived from empirical testing on a large heterogeneous corpora of benchmark instances might lead to sub-optimal performance. In this work, we propose an online learning approach that adapts the application of heuristics towards the single instance at hand. We replace the commonly used static heuristic handling with an adaptive framework exploiting past observations about the heuristic's behavior to make future decisions. In particular, we model the problem of controlling Large Neighborhood Search and Diving - two broad and complex classes of heuristics - as a multi-armed bandit problem. Going beyond existing work in the literature, we control two different classes of heuristics simultaneously by a single learning agent. We verify our approach numerically and show consistent node reductions over the MIPLIB 2017 Benchmark set. For harder instances that take at least 1000 seconds to solve, we observe a speedup of 4%.
SparseFormer: Sparse Visual Recognition via Limited Latent Tokens
Authors: Ziteng Gao, Zhan Tong, LiMin Wang, Mike Zheng Shou
Date: 2023-04-07 00:00:00
Tasks: Image Classification, Sparse Representation-based Classification, Video Classification

Human visual recognition is a sparse process, where only a few salient visual cues are attended to rather than traversing every detail uniformly. However, most current vision networks follow a dense paradigm, processing every single visual unit (e.g,, pixel or patch) in a uniform manner. In this paper, we challenge this dense paradigm and present a new method, coined SparseFormer, to imitate human's sparse visual recognition in an end-to-end manner. SparseFormer learns to represent images using a highly limited number of tokens (down to 49) in the latent space with sparse feature sampling procedure instead of processing dense units in the original pixel space. Therefore, SparseFormer circumvents most of dense operations on the image space and has much lower computational costs. Experiments on the ImageNet classification benchmark dataset show that SparseFormer achieves performance on par with canonical or well-established models while offering better accuracy-throughput tradeoff. Moreover, the design of our network can be easily extended to the video classification with promising performance at lower computational costs. We hope that our work can provide an alternative way for visual modeling and inspire further research on sparse neural architectures. The code will be publicly available at https://github.com/showlab/sparseformer
Embodied Concept Learner: Self-supervised Learning of Concepts and Mapping through Instruction Following
Authors: Mingyu Ding, Yan Xu, Zhenfang Chen, David Daniel Cox, Ping Luo, Joshua B. Tenenbaum, Chuang Gan
Date: 2023-04-07 00:00:00
Tasks: Instruction Following, Self-Supervised Learning

Humans, even at a very early age, can learn visual concepts and understand geometry and layout through active interaction with the environment, and generalize their compositions to complete tasks described by natural languages in novel scenes. To mimic such capability, we propose Embodied Concept Learner (ECL) in an interactive 3D environment. Specifically, a robot agent can ground visual concepts, build semantic maps and plan actions to complete tasks by learning purely from human demonstrations and language instructions, without access to ground-truth semantic and depth supervisions from simulations. ECL consists of: (i) an instruction parser that translates the natural languages into executable programs; (ii) an embodied concept learner that grounds visual concepts based on language descriptions; (iii) a map constructor that estimates depth and constructs semantic maps by leveraging the learned concepts; and (iv) a program executor with deterministic policies to execute each program. ECL has several appealing benefits thanks to its modularized design. Firstly, it enables the robotic agent to learn semantics and depth unsupervisedly acting like babies, e.g., ground concepts through active interaction and perceive depth by disparities when moving forward. Secondly, ECL is fully transparent and step-by-step interpretable in long-term planning. Thirdly, ECL could be beneficial for the embodied instruction following (EIF), outperforming previous works on the ALFRED benchmark when the semantic label is not provided. Also, the learned concept can be reused for other downstream tasks, such as reasoning of object states. Project page: http://ecl.csail.mit.edu/
Test your samples jointly: Pseudo-reference for image quality evaluation
Authors: Marcelin Tworski, Stéphane Lathuilière
Date: 2023-04-07 00:00:00
Tasks: Image Quality Assessment

In this paper, we address the well-known image quality assessment problem but in contrast from existing approaches that predict image quality independently for every images, we propose to jointly model different images depicting the same content to improve the precision of quality estimation. This proposal is motivated by the idea that multiple distorted images can provide information to disambiguate image features related to content and quality. To this aim, we combine the feature representations from the different images to estimate a pseudo-reference that we use to enhance score prediction. Our experiments show that at test-time, our method successfully combines the features from multiple images depicting the same new content, improving estimation quality.
Clutter Detection and Removal in 3D Scenes with View-Consistent Inpainting
Authors: Fangyin Wei, Thomas Funkhouser, Szymon Rusinkiewicz
Date: 2023-04-07 00:00:00
Proceeding: ICCV 2023 1
Tasks: 3D Inpainting, Data Augmentation

Removing clutter from scenes is essential in many applications, ranging from privacy-concerned content filtering to data augmentation. In this work, we present an automatic system that removes clutter from 3D scenes and inpaints with coherent geometry and texture. We propose techniques for its two key components: 3D segmentation from shared properties and 3D inpainting, both of which are important problems. The definition of 3D scene clutter (frequently-moving objects) is not well captured by commonly-studied object categories in computer vision. To tackle the lack of well-defined clutter annotations, we group noisy fine-grained labels, leverage virtual rendering, and impose an instance-level area-sensitive loss. Once clutter is removed, we inpaint geometry and texture in the resulting holes by merging inpainted RGB-D images. This requires novel voting and pruning strategies that guarantee multi-view consistency across individually inpainted images for mesh reconstruction. Experiments on ScanNet and Matterport dataset show that our method outperforms baselines for clutter segmentation and 3D inpainting, both visually and quantitatively.
Zero-shot CT Field-of-view Completion with Unconditional Generative Diffusion Prior
Authors: Kaiwen Xu, Aravind R. Krishnan, Thomas Z. Li, Yuankai Huo, Kim L. Sandler, Fabien Maldonado, Bennett A. Landman
Date: 2023-04-07 00:00:00
Tasks: Computed Tomography (CT)

Anatomically consistent field-of-view (FOV) completion to recover truncated body sections has important applications in quantitative analyses of computed tomography (CT) with limited FOV. Existing solution based on conditional generative models relies on the fidelity of synthetic truncation patterns at training phase, which poses limitations for the generalizability of the method to potential unknown types of truncation. In this study, we evaluate a zero-shot method based on a pretrained unconditional generative diffusion prior, where truncation pattern with arbitrary forms can be specified at inference phase. In evaluation on simulated chest CT slices with synthetic FOV truncation, the method is capable of recovering anatomically consistent body sections and subcutaneous adipose tissue measurement error caused by FOV truncation. However, the correction accuracy is inferior to the conditionally trained counterpart.
Language Models are Causal Knowledge Extractors for Zero-shot Video Question Answering
Authors: Hung-Ting Su, Yulei Niu, Xudong Lin, Winston H. Hsu, Shih-Fu Chang
Date: 2023-04-07 00:00:00
Tasks: Question Answering, Question Generation, Question-Generation, Reading Comprehension, Video Question Answering

Causal Video Question Answering (CVidQA) queries not only association or temporal relations but also causal relations in a video. Existing question synthesis methods pre-trained question generation (QG) systems on reading comprehension datasets with text descriptions as inputs. However, QG models only learn to ask association questions (e.g., ``what is someone doing...'') and result in inferior performance due to the poor transfer of association knowledge to CVidQA, which focuses on causal questions like ``why is someone doing ...''. Observing this, we proposed to exploit causal knowledge to generate question-answer pairs, and proposed a novel framework, Causal Knowledge Extraction from Language Models (CaKE-LM), leveraging causal commonsense knowledge from language models to tackle CVidQA. To extract knowledge from LMs, CaKE-LM generates causal questions containing two events with one triggering another (e.g., ``score a goal'' triggers ``soccer player kicking ball'') by prompting LM with the action (soccer player kicking ball) to retrieve the intention (to score a goal). CaKE-LM significantly outperforms conventional methods by 4% to 6% of zero-shot CVidQA accuracy on NExT-QA and Causal-VidQA datasets. We also conduct comprehensive analyses and provide key findings for future research.