pwc-archive/papers-with-abstracts huggingface dataset:
Open-World Visual Reasoning by a Neuro-Symbolic Program of Zero-Shot Symbols
Authors: Gertjan Burghouts, Fieke Hillerström, Erwin Walraven, Michael van Bekkum, Frank Ruis, Joris Sijs, Jelle van Mil, Judith Dijk
Date: 2024-07-18 00:00:00
Tasks: Visual Reasoning

We consider the problem of finding spatial configurations of multiple objects in images, e.g., a mobile inspection robot is tasked to localize abandoned tools on the floor. We define the spatial configuration of objects by first-order logic in terms of relations and attributes. A neuro-symbolic program matches the logic formulas to probabilistic object proposals for the given image, provided by language-vision models by querying them for the symbols. This work is the first to combine neuro-symbolic programming (reasoning) and language-vision models (learning) to find spatial configurations of objects in images in an open world setting. We show the effectiveness by finding abandoned tools on floors and leaking pipes. We find that most prediction errors are due to biases in the language-vision model.
Geometric Active Exploration in Markov Decision Processes: the Benefit of Abstraction
Authors: Riccardo De Santi, Federico Arangath Joseph, Noah Liniger, Mirco Mutti, Andreas Krause
Date: 2024-07-18 00:00:00
Tasks: Computational Efficiency, Reinforcement Learning (RL), scientific discovery

How can a scientist use a Reinforcement Learning (RL) algorithm to design experiments over a dynamical system's state space? In the case of finite and Markovian systems, an area called Active Exploration (AE) relaxes the optimization problem of experiments design into Convex RL, a generalization of RL admitting a wider notion of reward. Unfortunately, this framework is currently not scalable and the potential of AE is hindered by the vastness of experiment spaces typical of scientific discovery applications. However, these spaces are often endowed with natural geometries, e.g., permutation invariance in molecular design, that an agent could leverage to improve the statistical and computational efficiency of AE. To achieve this, we bridge AE and MDP homomorphisms, which offer a way to exploit known geometric structures via abstraction. Towards this goal, we make two fundamental contributions: we extend MDP homomorphisms formalism to Convex RL, and we present, to the best of our knowledge, the first analysis that formally captures the benefit of abstraction via homomorphisms on sample efficiency. Ultimately, we propose the Geometric Active Exploration (GAE) algorithm, which we analyse theoretically and experimentally in environments motivated by problems in scientific discovery.
Capturing Style in Author and Document Representation
Authors: Enzo Terreau, Antoine Gourru, Julien Velcin
Date: 2024-07-18 00:00:00
Tasks: Authorship Attribution, Recommendation Systems, Representation Learning

A wide range of Deep Natural Language Processing (NLP) models integrates continuous and low dimensional representations of words and documents. Surprisingly, very few models study representation learning for authors. These representations can be used for many NLP tasks, such as author identification and classification, or in recommendation systems. A strong limitation of existing works is that they do not explicitly capture writing style, making them hardly applicable to literary data. We therefore propose a new architecture based on Variational Information Bottleneck (VIB) that learns embeddings for both authors and documents with a stylistic constraint. Our model fine-tunes a pre-trained document encoder. We stimulate the detection of writing style by adding predefined stylistic features making the representation axis interpretable with respect to writing style indicators. We evaluate our method on three datasets: a literary corpus extracted from the Gutenberg Project, the Blog Authorship Corpus and IMDb62, for which we show that it matches or outperforms strong/recent baselines in authorship attribution while capturing much more accurately the authors stylistic aspects.
Reconstruct the Pruned Model without Any Retraining
Authors: Pingjie Wang, Ziqing Fan, Shengchao Hu, Zhe Chen, Yanfeng Wang, Yu Wang
Date: 2024-07-18 00:00:00
Tasks: Common Sense Reasoning, model

Structured pruning is a promising hardware-friendly compression technique for large language models (LLMs), which is expected to be retraining-free to avoid the enormous retraining cost. This retraining-free paradigm involves (1) pruning criteria to define the architecture and (2) distortion reconstruction to restore performance. However, existing methods often emphasize pruning criteria while using reconstruction techniques that are specific to certain modules or criteria, resulting in limited generalizability. To address this, we introduce the Linear Interpolation-based Adaptive Reconstruction (LIAR) framework, which is both efficient and effective. LIAR does not require back-propagation or retraining and is compatible with various pruning criteria and modules. By applying linear interpolation to the preserved weights, LIAR minimizes reconstruction error and effectively reconstructs the pruned output. Our evaluations on benchmarks such as GLUE, SQuAD, WikiText, and common sense reasoning show that LIAR enables a BERT model to maintain 98% accuracy even after removing 50% of its parameters and achieves top performance for LLaMA in just a few minutes.
RISC-V RVV efficiency for ANN algorithms
Authors: Konstantin Rumyantsev, Pavel Yakovlev, Andrey Gorshkov, Andrey P. Sokolov
Date: 2024-07-18 00:00:00
Tasks: CPU

Handling vast amounts of data is crucial in today's world. The growth of high-performance computing has created a need for parallelization, particularly in the area of machine learning algorithms such as ANN (Approximate Nearest Neighbors). To improve the speed of these algorithms, it is important to optimize them for specific processor architectures. RISC-V (Reduced Instruction Set Computer Five) is one of the modern processor architectures, which features a vector instruction set called RVV (RISC-V Vector Extension). In machine learning algorithms, vector extensions are widely utilized to improve the processing of voluminous data. This study examines the effectiveness of applying RVV to commonly used ANN algorithms. The algorithms were adapted for RISC-V and optimized using RVV after identifying the primary bottlenecks. Additionally, we developed a theoretical model of a parameterized vector block and identified the best on average configuration that demonstrates the highest theoretical performance of the studied ANN algorithms when the other CPU parameters are fixed.
Deterministic Trajectory Optimization through Probabilistic Optimal Control
Authors: Mohammad Mahmoudi Filabadi, Tom Lefebvre, Guillaume Crevecoeur
Date: 2024-07-18 00:00:00
Tasks: Uncertainty Quantification

In this article, we discuss two algorithms tailored to discrete-time deterministic finite-horizon nonlinear optimal control problems or so-called deterministic trajectory optimization problems. Both algorithms can be derived from an emerging theoretical paradigm that we refer to as probabilistic optimal control. The paradigm reformulates stochastic optimal control as an equivalent probabilistic inference problem and can be viewed as a generalisation of the former. The merit of this perspective is that it allows to address the problem using the Expectation-Maximization algorithm. It is shown that the application of this algorithm results in a fixed point iteration of probabilistic policies that converge to the deterministic optimal policy. Two strategies for policy evaluation are discussed, using state-of-the-art uncertainty quantification methods resulting into two distinct algorithms. The algorithms are structurally closest related to the differential dynamic programming algorithm and related methods that use sigma-point methods to avoid direct gradient evaluations. The main advantage of the algorithms is an improved balance between exploration and exploitation over the iterations, leading to improved numerical stability and accelerated convergence. These properties are demonstrated on different nonlinear systems.
A deep latent variable model for semi-supervised multi-unit soft sensing in industrial processes
Authors: Bjarne Grimstad, Kristian Løvland, Lars S. Imsland, Vidar Gunnerud
Date: 2024-07-18 00:00:00
Tasks: Multi-Task Learning

In many industrial processes, an apparent lack of data limits the development of data-driven soft sensors. There are, however, often opportunities to learn stronger models by being more data-efficient. To achieve this, one can leverage knowledge about the data from which the soft sensor is learned. Taking advantage of properties frequently possessed by industrial data, we introduce a deep latent variable model for semi-supervised multi-unit soft sensing. This hierarchical, generative model is able to jointly model different units, as well as learning from both labeled and unlabeled data. An empirical study of multi-unit soft sensing is conducted using two datasets: a synthetic dataset of single-phase fluid flow, and a large, real dataset of multi-phase flow in oil and gas wells. We show that by combining semi-supervised and multi-task learning, the proposed model achieves superior results, outperforming current leading methods for this soft sensing problem. We also show that when a model has been trained on a multi-unit dataset, it may be finetuned to previously unseen units using only a handful of data points. In this finetuning procedure, unlabeled data improve soft sensor performance; remarkably, this is true even when no labeled data are available.
Mean Teacher based SSL Framework for Indoor Localization Using Wi-Fi RSSI Fingerprinting
Authors: Sihao Li, Zhe Tang, Kyeong Soo Kim, Jeremy S. Smith
Date: 2024-07-18 00:00:00
Tasks: Indoor Localization

Wi-Fi fingerprinting is widely applied for indoor localization due to the widespread availability of Wi-Fi devices. However, traditional methods are not ideal for multi-building and multi-floor environments due to the scalability issues. Therefore, more and more researchers have employed deep learning techniques to enable scalable indoor localization. This paper introduces a novel semi-supervised learning framework for neural networks based on wireless access point selection, noise injection, and Mean Teacher model, which leverages unlabeled fingerprints to enhance localization performance. The proposed framework can manage hybrid in/outsourcing and voluntarily contributed databases and continually expand the fingerprint database with newly submitted unlabeled fingerprints during service. The viability of the proposed framework was examined using two established deep-learning models with the UJIIndoorLoc database. The experimental results suggest that the proposed framework significantly improves localization performance compared to the supervised learning-based approach in terms of floor-level coordinate estimation using EvAAL metric. It shows enhancements up to 10.99% and 8.98% in the former scenario and 4.25% and 9.35% in the latter, respectively with additional studies highlight the importance of the essential components of the proposed framework.
Scikit-fingerprints: easy and efficient computation of molecular fingerprints in Python
Authors: Jakub Adamczyk, Piotr Ludynia
Date: 2024-07-18 00:00:00
Tasks: Molecular Property Prediction, Property Prediction

In this work, we present scikit-fingerprints, a Python package for computation of molecular fingerprints for applications in chemoinformatics. Our library offers an industry-standard scikit-learn interface, allowing intuitive usage and easy integration with machine learning pipelines. It is also highly optimized, featuring parallel computation that enables efficient processing of large molecular datasets. Currently, scikit-fingerprints stands as the most feature-rich library in the open source Python ecosystem, offering over 30 molecular fingerprints. Our library simplifies chemoinformatics tasks based on molecular fingerprints, including molecular property prediction and virtual screening. It is also flexible, highly efficient, and fully open source.
Hierarchical Stage-Wise Training of Linked Deep Neural Networks for Multi-Building and Multi-Floor Indoor Localization Based on Wi-Fi RSSI Fingerprinting
Authors: Sihao Li, Kyeong Soo Kim, Zhe Tang, Graduate, Jeremy S. Smith
Date: 2024-07-18 00:00:00
Tasks: Indoor Localization

In this paper, we present a new solution to the problem of large-scale multi-building and multi-floor indoor localization based on linked neural networks, where each neural network is dedicated to a sub-problem and trained under a hierarchical stage-wise training framework. When the measured data from sensors have a hierarchical representation as in multi-building and multi-floor indoor localization, it is important to exploit the hierarchical nature in data processing to provide a scalable solution. In this regard, the hierarchical stage-wise training framework extends the original stage-wise training framework to the case of multiple linked networks by training a lower-hierarchy network based on the prior knowledge gained from the training of higher-hierarchy networks. The experimental results with the publicly-available UJIIndoorLoc multi-building and multi-floor Wi-Fi RSSI fingerprint database demonstrate that the linked neural networks trained under the proposed hierarchical stage-wise training framework can achieve a three-dimensional localization error of 8.19 m, which, to the best of the authors' knowledge, is the most accurate result ever obtained for neural network-based models trained and evaluated with the full datasets of the UJIIndoorLoc database, and that, when applied to a model based on hierarchical convolutional neural networks, the proposed training framework can also significantly reduce the three-dimensional localization error from 11.78 m to 8.71 m.
Auditing Local Explanations is Hard
Authors: Robi Bhattacharjee, Ulrike Von Luxburg
Date: 2024-07-18 00:00:00

In sensitive contexts, providers of machine learning algorithms are increasingly required to give explanations for their algorithms' decisions. However, explanation receivers might not trust the provider, who potentially could output misleading or manipulated explanations. In this work, we investigate an auditing framework in which a third-party auditor or a collective of users attempts to sanity-check explanations: they can query model decisions and the corresponding local explanations, pool all the information received, and then check for basic consistency properties. We prove upper and lower bounds on the amount of queries that are needed for an auditor to succeed within this framework. Our results show that successful auditing requires a potentially exorbitant number of queries -- particularly in high dimensional cases. Our analysis also reveals that a key property is the ``locality'' of the provided explanations -- a quantity that so far has not been paid much attention to in the explainability literature. Looking forward, our results suggest that for complex high-dimensional settings, merely providing a pointwise prediction and explanation could be insufficient, as there is no way for the users to verify that the provided explanations are not completely made-up.
Analyzing and Bridging the Gap between Maximizing Total Reward and Discounted Reward in Deep Reinforcement Learning
Authors: Shuyu Yin, Fei Wen, Peilin Liu, Tao Luo
Date: 2024-07-18 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning

In deep reinforcement learning applications, maximizing discounted reward is often employed instead of maximizing total reward to ensure the convergence and stability of algorithms, even though the performance metric for evaluating the policy remains the total reward. However, the optimal policies corresponding to these two objectives may not always be consistent. To address this issue, we analyzed the suboptimality of the policy obtained through maximizing discounted reward in relation to the policy that maximizes total reward and identified the influence of hyperparameters. Additionally, we proposed sufficient conditions for aligning the optimal policies of these two objectives under various settings. The primary contributions are as follows: We theoretically analyzed the factors influencing performance when using discounted reward as a proxy for total reward, thereby enhancing the theoretical understanding of this scenario. Furthermore, we developed methods to align the optimal policies of the two objectives in certain situations, which can improve the performance of reinforcement learning algorithms.
Deep Time Series Models: A Comprehensive Survey and Benchmark
Authors: Yuxuan Wang, Haixu Wu, Jiaxiang Dong, Yong liu, Mingsheng Long, Jianmin Wang
Date: 2024-07-18 00:00:00
Tasks: Survey, Time Series

Time series, characterized by a sequence of data points arranged in a discrete-time order, are ubiquitous in real-world applications. Different from other modalities, time series present unique challenges due to their complex and dynamic nature, including the entanglement of nonlinear patterns and time-variant trends. Analyzing time series data is of great significance in real-world scenarios and has been widely studied over centuries. Recent years have witnessed remarkable breakthroughs in the time series community, with techniques shifting from traditional statistical methods to advanced deep learning models. In this paper, we delve into the design of deep time series models across various analysis tasks and review the existing literature from two perspectives: basic modules and model architectures. Further, we develop and release Time Series Library (TSLib) as a fair benchmark of deep time series models for diverse analysis tasks, which implements 24 mainstream models, covers 30 datasets from different domains, and supports five prevalent analysis tasks. Based on TSLib, we thoroughly evaluate 12 advanced deep time series models on different tasks. Empirical results indicate that models with specific structures are well-suited for distinct analytical tasks, which offers insights for research and adoption of deep time series models. Code is available at https://github.com/thuml/Time-Series-Library.
Motif-Consistent Counterfactuals with Adversarial Refinement for Graph-Level Anomaly Detection
Authors: Chunjing Xiao, Shikang Pang, Wenxin Tai, Yanlong Huang, Goce Trajcevski, Fan Zhou
Date: 2024-07-18 00:00:00
Tasks: Anomaly Detection, counterfactual, Generative Adversarial Network

Graph-level anomaly detection is significant in diverse domains. To improve detection performance, counterfactual graphs have been exploited to benefit the generalization capacity by learning causal relations. Most existing studies directly introduce perturbations (e.g., flipping edges) to generate counterfactual graphs, which are prone to alter the semantics of generated examples and make them off the data manifold, resulting in sub-optimal performance. To address these issues, we propose a novel approach, Motif-consistent Counterfactuals with Adversarial Refinement (MotifCAR), for graph-level anomaly detection. The model combines the motif of one graph, the core subgraph containing the identification (category) information, and the contextual subgraph (non-motif) of another graph to produce a raw counterfactual graph. However, the produced raw graph might be distorted and cannot satisfy the important counterfactual properties: Realism, Validity, Proximity and Sparsity. Towards that, we present a Generative Adversarial Network (GAN)-based graph optimizer to refine the raw counterfactual graphs. It adopts the discriminator to guide the generator to generate graphs close to realistic data, i.e., meet the property Realism. Further, we design the motif consistency to force the motif of the generated graphs to be consistent with the realistic graphs, meeting the property Validity. Also, we devise the contextual loss and connection loss to control the contextual subgraph and the newly added links to meet the properties Proximity and Sparsity. As a result, the model can generate high-quality counterfactual graphs. Experiments demonstrate the superiority of MotifCAR.
Transformers with Stochastic Competition for Tabular Data Modelling
Authors: Andreas Voskou, Charalambos Christoforou, Sotirios Chatzis
Date: 2024-07-18 00:00:00
Tasks: Deep Learning, tabular-classification, tabular-regression

Despite the prevalence and significance of tabular data across numerous industries and fields, it has been relatively underexplored in the realm of deep learning. Even today, neural networks are often overshadowed by techniques such as gradient boosted decision trees (GBDT). However, recent models are beginning to close this gap, outperforming GBDT in various setups and garnering increased attention in the field. Inspired by this development, we introduce a novel stochastic deep learning model specifically designed for tabular data. The foundation of this model is a Transformer-based architecture, carefully adapted to cater to the unique properties of tabular data through strategic architectural modifications and leveraging two forms of stochastic competition. First, we employ stochastic "Local Winner Takes All" units to promote generalization capacity through stochasticity and sparsity. Second, we introduce a novel embedding layer that selects among alternative linear embedding layers through a mechanism of stochastic competition. The effectiveness of the model is validated on a variety of widely-used, publicly available datasets. We demonstrate that, through the incorporation of these elements, our model yields high performance and marks a significant advancement in the application of deep learning to tabular data.
Evaluating Large Language Models for Anxiety and Depression Classification using Counseling and Psychotherapy Transcripts
Authors: Junwei Sun, Siqi Ma, Yiran Fan, Peter Washington
Date: 2024-07-18 00:00:00
Tasks: Feature Engineering

We aim to evaluate the efficacy of traditional machine learning and large language models (LLMs) in classifying anxiety and depression from long conversational transcripts. We fine-tune both established transformer models (BERT, RoBERTa, Longformer) and more recent large models (Mistral-7B), trained a Support Vector Machine with feature engineering, and assessed GPT models through prompting. We observe that state-of-the-art models fail to enhance classification outcomes compared to traditional machine learning methods.
Non-Contact Breath Rate Classification Using SVM Model and mmWave Radar Sensor Data
Authors: Mohammad Wassaf Ali, Ayushi Gupta, Mujeev Khan, Mohd Wajid
Date: 2024-07-18 00:00:00

This work presents the use of frequency modulated continuous wave (FMCW) radar technology combined with a machine learning model to differentiate between normal and abnormal breath rates. The proposed system non-contactly collects data using FMCW radar, which depends on breath rates. Various support vector machine kernels are used to classify the observed data into normal and abnormal states. Prolonged experiments show good accuracy in breath rate classification, confirming the model's efficacy. The best accuracy is 95 percent with the smallest number of support vectors in the case of the quadratic polynomial kernel.