pwc-archive/papers-with-abstracts huggingface dataset:
Q-ARDNS-Multi: A Multi-Agent Quantum Reinforcement Learning Framework with Meta-Cognitive Adaptation for Complex 3D Environments
Authors: Umberto Gonçalves de Sousa
Date: 2025-06-02 00:00:00
Tasks: Autonomous Navigation, Collision Avoidance, Computational Efficiency, Decision Making Under Uncertainty

This paper presents Q-ARDNS-Multi, an advanced multi-agent quantum reinforcement learning (QRL) framework that extends the ARDNS-FN-Quantum model, where Q-ARDNS-Multi stands for "Quantum Adaptive Reward-Driven Neural Simulator - Multi-Agent". It integrates quantum circuits with RY gates, meta-cognitive adaptation, and multi-agent coordination mechanisms for complex 3D environments. Q-ARDNS-Multi leverages a 2-qubit quantum circuit for action selection, a dual-memory system inspired by human cognition, a shared memory module for agent cooperation, and adaptive exploration strategies modulated by reward variance and intrinsic motivation. Evaluated in a $10 \times 10 \times 3$ GridWorld environment with two agents over 5000 episodes, Q-ARDNS-Multi achieves success rates of 99.6\% and 99.5\% for Agents 0 and 1, respectively, outperforming Multi-Agent Deep Deterministic Policy Gradient (MADDPG) and Soft Actor-Critic (SAC) in terms of success rate, stability, navigation efficiency, and collision avoidance. The framework records mean rewards of $-304.2891 \pm 756.4636$ and $-295.7622 \pm 752.7103$, averaging 210 steps to goal, demonstrating its robustness in dynamic settings. Comprehensive analyses, including learning curves, reward distributions, statistical tests, and computational efficiency evaluations, highlight the contributions of quantum circuits and meta-cognitive adaptation. By bridging quantum computing, cognitive science, and multi-agent RL, Q-ARDNS-Multi offers a scalable, human-like approach for applications in robotics, autonomous navigation, and decision-making under uncertainty.
Fodor and Pylyshyn's Legacy -- Still No Human-like Systematic Compositionality in Neural Networks
Authors: Tim Woydt, Moritz Willig, Antonia Wüst, Lukas Helff, Wolfgang Stammer, Constantin A. Rothkopf, Kristian Kersting
Date: 2025-06-02 00:00:00
Tasks: Meta-Learning

Strong meta-learning capabilities for systematic compositionality are emerging as an important skill for navigating the complex and changing tasks of today's world. However, in presenting models for robust adaptation to novel environments, it is important to refrain from making unsupported claims about the performance of meta-learning systems that ultimately do not stand up to scrutiny. While Fodor and Pylyshyn famously posited that neural networks inherently lack this capacity as they are unable to model compositional representations or structure-sensitive operations, and thus are not a viable model of the human mind, Lake and Baroni recently presented meta-learning as a pathway to compositionality. In this position paper, we critically revisit this claim and highlight limitations in the proposed meta-learning framework for compositionality. Our analysis shows that modern neural meta-learning systems can only perform such tasks, if at all, under a very narrow and restricted definition of a meta-learning setup. We therefore claim that `Fodor and Pylyshyn's legacy' persists, and to date, there is no human-like systematic compositionality learned in neural networks.
Optimization Strategies for Variational Quantum Algorithms in Noisy Landscapes
Authors: Vojtěch Novák, Ivan Zelinka, Václav Snášel
Date: 2025-06-02 00:00:00
Tasks: Navigate, Quantum Machine Learning

Variational Quantum Algorithms (VQAs) are a promising tool in the NISQ era, leveraging quantum computing across diverse fields. However, their performance is hindered by optimization challenges like local minima, barren plateaus, and noise from current quantum hardware. Variational Quantum Eigensolver (VQE), a key subset of VQAs, approximates molecular ground-state energies by minimizing a Hamiltonian, enabling quantum chemistry applications. Beyond this, VQE contributes to condensed matter physics by exploring quantum phase transitions and exotic states, and to quantum machine learning by optimizing parameterized circuits for classifiers and generative models. This study systematically evaluates over 50 meta-heuristic optimization algorithms including evolution-based, swarm-based, and music-inspired methods-on their ability to navigate VQE's multimodal and noisy landscapes. Using a multi-phase sieve-like approach, we identify the most capable optimizers and compare their performance on a 1D Ising model (3-9 qubits). Further testing on the Hubbard model (up to 192 parameters) reveals insights into convergence rates, effectiveness, and resilience under noise, offering valuable guidance for advancing optimization in noisy quantum environments.
Temporal Variational Implicit Neural Representations
Authors: Batuhan Koyuncu, Rachael DeVries, Ole Winther, Isabel Valera
Date: 2025-06-02 00:00:00
Tasks: Imputation, Meta-Learning, Time Series

We introduce Temporal Variational Implicit Neural Representations (TV-INRs), a probabilistic framework for modeling irregular multivariate time series that enables efficient individualized imputation and forecasting. By integrating implicit neural representations with latent variable models, TV-INRs learn distributions over time-continuous generator functions conditioned on signal-specific covariates. Unlike existing approaches that require extensive training, fine-tuning or meta-learning, our method achieves accurate individualized predictions through a single forward pass. Our experiments demonstrate that with a single TV-INRs instance, we can accurately solve diverse imputation and forecasting tasks, offering a computationally efficient and scalable solution for real-world applications. TV-INRs excel especially in low-data regimes, where it outperforms existing methods by an order of magnitude in mean squared error for imputation task.
Driving factors of auditory category learning success
Authors: Nan Wang, Gangyi Feng
Date: 2025-06-02 00:00:00

Our brain learns to update its mental model of the environment by abstracting sensory experiences for adaptation and survival. Learning to categorize sounds is one essential abstracting process for high-level human cognition, such as speech perception, but it is also challenging due to the variable nature of auditory signals and their dynamic contexts. To overcome these learning challenges and enhance learner performance, it is essential to identify the impact of learning-related factors in developing better training protocols. Here, we conducted an extensive meta-analysis of auditory category learning studies, including a total of 111 experiments and 4,521 participants, and examined to what extent three hidden factors (i.e., variability, intensity, and engagement) derived from 12 experimental variables contributed to learning success (i.e., effect sizes). Variables related to intensity and training variability outweigh others in predicting learning effect size. Activation likelihood estimation (ALE) meta-analysis of the neuroimaging studies revealed training-induced systematic changes in the frontotemporal-parietal networks. Increased brain activities in speech and motor-related auditory-frontotemporal regions and decreased activities in cuneus and precuneus areas are associated with increased learning effect sizes. These findings not only enhance our understanding of the driving forces behind speech and auditory category learning success, along with its neural changes, but also guide researchers and practitioners in designing more effective training protocols that consider the three key aspects of learning to facilitate learner success.
A 2-Stage Model for Vehicle Class and Orientation Detection with Photo-Realistic Image Generation
Authors: Youngmin Kim, Donghwa Kang, Hyeongboo Baek
Date: 2025-06-02 00:00:00
Tasks: Image Generation

We aim to detect the class and orientation of a vehicle by training a model with synthetic data. However, the distribution of the classes in the training data is imbalanced, and the model trained on the synthetic image is difficult to predict in real-world images. We propose a two-stage detection model with photo-realistic image generation to tackle this issue. Our model mainly takes four steps to detect the class and orientation of the vehicle. (1) It builds a table containing the image, class, and location information of objects in the image, (2) transforms the synthetic images into real-world images style, and merges them into the meta table. (3) Classify vehicle class and orientation using images from the meta-table. (4) Finally, the vehicle class and orientation are detected by combining the pre-extracted location information and the predicted classes. We achieved 4th place in IEEE BigData Challenge 2022 Vehicle class and Orientation Detection (VOD) with our approach.
TSRating: Rating Quality of Diverse Time Series Data by Meta-learning from LLM Judgment
Authors: Shunyu Wu, Dan Li, Haozheng Ye, Zhuomin Chen, Jiahui Zhou, Jian Lou, Zibin Zheng, See-Kiong Ng
Date: 2025-06-02 00:00:00
Tasks: Meta-Learning, Time Series

High-quality time series (TS) data are essential for ensuring TS model performance, rendering research on rating TS data quality indispensable. Existing methods have shown promising rating accuracy within individual domains, primarily by extending data quality rating techniques such as influence functions and Shapley values to account for temporal characteristics. However, they neglect the fact that real-world TS data can span vastly different domains and exhibit distinct properties, hampering the accurate and efficient rating of diverse TS data. In this paper, we propose TSRating, a novel and unified framework for rating the quality of time series data crawled from diverse domains. TSRating is built on the assumption that LLMs inherit ample knowledge, acquired during their extensive pretraining, enabling them to comprehend and discern quality differences in diverse TS data. We verify this assumption by devising a series of prompts to elicit quality comparisons from LLMs for pairs of TS samples. We then fit a dedicated rating model, termed TSRater, to convert the LLMs' judgments into efficient quality predictions via TSRater's inference on future TS samples. To ensure cross-domain adaptability, we develop a meta-learning scheme to train TSRater on quality comparisons collected from nine distinct domains. To improve training efficiency, we employ signSGD for inner-loop updates, thus circumventing the demanding computation of hypergradients. Extensive experimental results on eleven benchmark datasets across three time series tasks, each using both conventional TS models and TS foundation models, demonstrate that TSRating outperforms baselines in terms of estimation accuracy, efficiency, and domain adaptability.
EV-Flying: an Event-based Dataset for In-The-Wild Recognition of Flying Objects
Authors: Gabriele Magrini, Federico Becattini, Giovanni Colombo, Pietro Pala
Date: 2025-06-04 00:00:00
Tasks: Event-based vision, Object Recognition

Monitoring aerial objects is crucial for security, wildlife conservation, and environmental studies. Traditional RGB-based approaches struggle with challenges such as scale variations, motion blur, and high-speed object movements, especially for small flying entities like insects and drones. In this work, we explore the potential of event-based vision for detecting and recognizing flying objects, in particular animals that may not follow short and long-term predictable patters. Event cameras offer high temporal resolution, low latency, and robustness to motion blur, making them well-suited for this task. We introduce EV-Flying, an event-based dataset of flying objects, comprising manually annotated birds, insects and drones with spatio-temporal bounding boxes and track identities. To effectively process the asynchronous event streams, we employ a point-based approach leveraging lightweight architectures inspired by PointNet. Our study investigates the classification of flying objects using point cloud-based event representations. The proposed dataset and methodology pave the way for more efficient and reliable aerial object recognition in real-world scenarios.
Learning from Noise: Enhancing DNNs for Event-Based Vision through Controlled Noise Injection
Authors: Marcin Kowalczyk, Kamil Jeziorek, Tomasz Kryjak
Date: 2025-06-04 00:00:00
Tasks: Event-based vision

Event-based sensors offer significant advantages over traditional frame-based cameras, especially in scenarios involving rapid motion or challenging lighting conditions. However, event data frequently suffers from considerable noise, negatively impacting the performance and robustness of deep learning models. Traditionally, this problem has been addressed by applying filtering algorithms to the event stream, but this may also remove some of relevant data. In this paper, we propose a novel noise-injection training methodology designed to enhance the neural networks robustness against varying levels of event noise. Our approach introduces controlled noise directly into the training data, enabling models to learn noise-resilient representations. We have conducted extensive evaluations of the proposed method using multiple benchmark datasets (N-Caltech101, N-Cars, and Mini N-ImageNet) and various network architectures, including Convolutional Neural Networks, Vision Transformers, Spiking Neural Networks, and Graph Convolutional Networks. Experimental results show that our noise-injection training strategy achieves stable performance over a range of noise intensities, consistently outperforms event-filtering techniques, and achieves the highest average classification accuracy, making it a viable alternative to traditional event-data filtering methods in an object classification system. Code: https://github.com/vision-agh/DVS_Filtering
YOND: Practical Blind Raw Image Denoising Free from Camera-Specific Data Dependency
Authors: Hansen Feng, Lizhi Wang, Yiqi Huang, Tong Li, Lin Zhu, Hua Huang
Date: 2025-06-04 00:00:00
Tasks: Denoising, Image Denoising, Noise Estimation

The rapid advancement of photography has created a growing demand for a practical blind raw image denoising method. Recently, learning-based methods have become mainstream due to their excellent performance. However, most existing learning-based methods suffer from camera-specific data dependency, resulting in performance drops when applied to data from unknown cameras. To address this challenge, we introduce a novel blind raw image denoising method named YOND, which represents You Only Need a Denoiser. Trained solely on synthetic data, YOND can generalize robustly to noisy raw images captured by diverse unknown cameras. Specifically, we propose three key modules to guarantee the practicality of YOND: coarse-to-fine noise estimation (CNE), expectation-matched variance-stabilizing transform (EM-VST), and SNR-guided denoiser (SNR-Net). Firstly, we propose CNE to identify the camera noise characteristic, refining the estimated noise parameters based on the coarse denoised image. Secondly, we propose EM-VST to eliminate camera-specific data dependency, correcting the bias expectation of VST according to the noisy image. Finally, we propose SNR-Net to offer controllable raw image denoising, supporting adaptive adjustments and manual fine-tuning. Extensive experiments on unknown cameras, along with flexible solutions for challenging cases, demonstrate the superior practicality of our method. The source code will be publicly available at the \href{https://fenghansen.github.io/publication/YOND}{project homepage}.
Matrix Is All You Need
Authors: Yuzhou Zhu
Date: 2025-05-11 00:00:00
Tasks: All, GPU, image-classification, Image Classification, Language Modeling, Language Modelling, Time Series Forecasting

Deep neural networks employ specialized architectures for vision, sequential and language tasks, yet this proliferation obscures their underlying commonalities. We introduce a unified matrix-order framework that casts convolutional, recurrent and self-attention operations as sparse matrix multiplications. Convolution is realized via an upper-triangular weight matrix performing first-order transformations; recurrence emerges from a lower-triangular matrix encoding stepwise updates; attention arises naturally as a third-order tensor factorization. We prove algebraic isomorphism with standard CNN, RNN and Transformer layers under mild assumptions. Empirical evaluations on image classification (MNIST, CIFAR-10/100, Tiny ImageNet), time-series forecasting (ETTh1, Electricity Load Diagrams) and language modeling/classification (AG News, WikiText-2, Penn Treebank) confirm that sparse-matrix formulations match or exceed native model performance while converging in comparable or fewer epochs. By reducing architecture design to sparse pattern selection, our matrix perspective aligns with GPU parallelism and leverages mature algebraic optimization tools. This work establishes a mathematically rigorous substrate for diverse neural architectures and opens avenues for principled, hardware-aware network design.
Uni-LoRA: One Vector is All You Need
Authors: Kaiyang Li, Shaobo Han, Qing Su, Wei Li, Zhipeng Cai, Shihao Ji
Date: 2025-06-01 00:00:00
Tasks: All, Mathematical Reasoning, parameter-efficient fine-tuning

Low-Rank Adaptation (LoRA) has become the de facto parameter-efficient fine-tuning (PEFT) method for large language models (LLMs) by constraining weight updates to low-rank matrices. Recent works such as Tied-LoRA, VeRA, and VB-LoRA push efficiency further by introducing additional constraints to reduce the trainable parameter space. In this paper, we show that the parameter space reduction strategies employed by these LoRA variants can be formulated within a unified framework, Uni-LoRA, where the LoRA parameter space, flattened as a high-dimensional vector space $R^D$, can be reconstructed through a projection from a subspace R^d, with $d \ll D$. We demonstrate that the fundamental difference among various LoRA methods lies in the choice of the projection matrix, $P \in R^{D \times d}$.Most existing LoRA variants rely on layer-wise or structure-specific projections that limit cross-layer parameter sharing, thereby compromising parameter efficiency. In light of this, we introduce an efficient and theoretically grounded projection matrix that is isometric, enabling global parameter sharing and reducing computation overhead. Furthermore, under the unified view of Uni-LoRA, this design requires only a single trainable vector to reconstruct LoRA parameters for the entire LLM - making Uni-LoRA both a unified framework and a "one-vector-only" solution. Extensive experiments on GLUE, mathematical reasoning, and instruction tuning benchmarks demonstrate that Uni-LoRA achieves state-of-the-art parameter efficiency while outperforming or matching prior approaches in predictive performance.
Stop Chasing the C-index: This Is How We Should Evaluate Our Survival Models
Authors: Christian Marius Lillelund, Shi-ang Qi, Russell Greiner, Christian Fischer Pedersen
Date: 2025-06-02 00:00:00
Tasks: Survival Analysis

We argue that many survival analysis and time-to-event models are incorrectly evaluated. First, we survey many examples of evaluation approaches in the literature and find that most rely on concordance (C-index). However, the C-index only measures a model's discriminative ability and does not assess other important aspects, such as the accuracy of the time-to-event predictions or the calibration of the model's probabilistic estimates. Next, we present a set of key desiderata for choosing the right evaluation metric and discuss their pros and cons. These are tailored to the challenges in survival analysis, such as sensitivity to miscalibration and various censoring assumptions. We hypothesize that the current development of survival metrics conforms to a double-helix ladder, and that model validity and metric validity must stand on the same rung of the assumption ladder. Finally, we discuss the appropriate methods for evaluating a survival model in practice and summarize various viewpoints opposing our analysis.
AceVFI: A Comprehensive Survey of Advances in Video Frame Interpolation
Authors: Dahyeon Kye, Changhyun Roh, Sukhun Ko, Chanho Eom, Jihyong Oh
Date: 2025-06-01 00:00:00
Tasks: Mamba, Motion Compensation, Survey, Video Frame Interpolation

Video Frame Interpolation (VFI) is a fundamental Low-Level Vision (LLV) task that synthesizes intermediate frames between existing ones while maintaining spatial and temporal coherence. VFI techniques have evolved from classical motion compensation-based approach to deep learning-based approach, including kernel-, flow-, hybrid-, phase-, GAN-, Transformer-, Mamba-, and more recently diffusion model-based approach. We introduce AceVFI, the most comprehensive survey on VFI to date, covering over 250+ papers across these approaches. We systematically organize and describe VFI methodologies, detailing the core principles, design assumptions, and technical characteristics of each approach. We categorize the learning paradigm of VFI methods namely, Center-Time Frame Interpolation (CTFI) and Arbitrary-Time Frame Interpolation (ATFI). We analyze key challenges of VFI such as large motion, occlusion, lighting variation, and non-linear motion. In addition, we review standard datasets, loss functions, evaluation metrics. We examine applications of VFI including event-based, cartoon, medical image VFI and joint VFI with other LLV tasks. We conclude by outlining promising future research directions to support continued progress in the field. This survey aims to serve as a unified reference for both newcomers and experts seeking a deep understanding of modern VFI landscapes.
A Review on Coarse to Fine-Grained Animal Action Recognition
Authors: Ali Zia, Renuka Sharma, Abdelwahed Khamis, Xuesong Li, Muhammad Husnain, Numan Shafi, Saeed Anwar, Sabine Schmoelzl, Eric Stone, Lars Petersson, Vivien Rolland
Date: 2025-06-01 00:00:00
Tasks: Action Recognition, Animal Action Recognition, Fine-grained Action Recognition, Temporal Action Localization

This review provides an in-depth exploration of the field of animal action recognition, focusing on coarse-grained (CG) and fine-grained (FG) techniques. The primary aim is to examine the current state of research in animal behaviour recognition and to elucidate the unique challenges associated with recognising subtle animal actions in outdoor environments. These challenges differ significantly from those encountered in human action recognition due to factors such as non-rigid body structures, frequent occlusions, and the lack of large-scale, annotated datasets. The review begins by discussing the evolution of human action recognition, a more established field, highlighting how it progressed from broad, coarse actions in controlled settings to the demand for fine-grained recognition in dynamic environments. This shift is particularly relevant for animal action recognition, where behavioural variability and environmental complexity present unique challenges that human-centric models cannot fully address. The review then underscores the critical differences between human and animal action recognition, with an emphasis on high intra-species variability, unstructured datasets, and the natural complexity of animal habitats. Techniques like spatio-temporal deep learning frameworks (e.g., SlowFast) are evaluated for their effectiveness in animal behaviour analysis, along with the limitations of existing datasets. By assessing the strengths and weaknesses of current methodologies and introducing a recently-published dataset, the review outlines future directions for advancing fine-grained action recognition, aiming to improve accuracy and generalisability in behaviour analysis across species.