Split (2)

train · 433k rows

id stringlengths 9 16	title stringlengths 4 278	abstract stringlengths 3 4.08k	cs.HC bool 2 classes	cs.CE bool 2 classes	cs.SD bool 2 classes	cs.SI bool 2 classes	cs.AI bool 2 classes	cs.IR bool 2 classes	cs.LG bool 2 classes	cs.RO bool 2 classes	cs.CL bool 2 classes	cs.IT bool 2 classes	cs.SY bool 2 classes	cs.CV bool 2 classes	cs.CR bool 2 classes	cs.CY bool 2 classes	cs.MA bool 2 classes	cs.NE bool 2 classes	cs.DB bool 2 classes	Other bool 2 classes	__index_level_0__ int64 0 541k
2210.11670	SIT at MixMT 2022: Fluent Translation Built on Giant Pre-trained Models	This paper describes the Stevens Institute of Technology's submission for the WMT 2022 Shared Task: Code-mixed Machine Translation (MixMT). The task consisted of two subtasks, subtask $1$ Hindi/English to Hinglish and subtask $2$ Hinglish to English translation. Our findings lie in the improvements made through the use...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	325,402
2202.04893	Differential Private Knowledge Transfer for Privacy-Preserving Cross-Domain Recommendation	Cross Domain Recommendation (CDR) has been popularly studied to alleviate the cold-start and data sparsity problem commonly existed in recommender systems. CDR models can improve the recommendation performance of a target domain by leveraging the data of other source domains. However, most existing CDR models assume in...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	279,708
0902.1629	Improvements of real coded genetic algorithms based on differential operators preventing premature convergence	This paper presents several types of evolutionary algorithms (EAs) used for global optimization on real domains. The interest has been focused on multimodal problems, where the difficulties of a premature convergence usually occurs. First the standard genetic algorithm (SGA) using binary encoding of real values and its...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	false	false	3,135
2311.12284	Model Predictive Control for Aggressive Driving Over Uneven Terrain	Terrain traversability in unstructured off-road autonomy has traditionally relied on semantic classification, resource-intensive dynamics models, or purely geometry-based methods to predict vehicle-terrain interactions. While inconsequential at low speeds, uneven terrain subjects our full-scale system to safety-critica...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	409,272
2502.05878	Enhancing Financial Time-Series Forecasting with Retrieval-Augmented Large Language Models	Stock movement prediction, a critical task in financial time-series forecasting, relies on identifying and retrieving key influencing factors from vast and complex datasets. However, traditional text-trained or numeric similarity-based retrieval methods often struggle to handle the intricacies of financial data. To add...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	531,820
2109.11750	Indoor Localization Using Smartphone Magnetic with Multi-Scale TCN and LSTM	A novel multi-scale temporal convolutional network (TCN) and long short-term memory network (LSTM) based magnetic localization approach is proposed. To enhance the discernibility of geomagnetic signals, the time-series preprocessing approach is constructed at first. Next, the TCN is invoked to expand the feature dimens...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	257,046
2403.08788	Verification for Object Detection -- IBP IoU	We introduce a novel Interval Bound Propagation (IBP) approach for the formal verification of object detection models, specifically targeting the Intersection over Union (IoU) metric. The approach has been implemented in an open source code, named IBP IoU, compatible with popular abstract interpretation based verificat...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	true	false	false	437,474
2409.05203	CARDinality: Interactive Card-shaped Robots with Locomotion and Haptics using Vibration	This paper introduces a novel approach to interactive robots by leveraging the form-factor of cards to create thin robots equipped with vibrational capabilities for locomotion and haptic feedback. The system is composed of flat-shaped robots with on-device sensing and wireless control, which offer lightweight portabili...	true	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	486,676
2501.05006	CHASE: A Native Relational Database for Hybrid Queries on Structured and Unstructured Data	Querying both structured and unstructured data has become a new paradigm in data analytics and recommendation. With unstructured data, such as text and videos, are converted to high-dimensional vectors and queried with approximate nearest neighbor search (ANNS). State-of-the-art database systems implement vector search...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	523,430
2104.04728	Discovering Categorical Main and Interaction Effects Based on Association Rule Mining	With the growing size of data sets, feature selection becomes increasingly important. Taking interactions of original features into consideration will lead to extremely high dimension, especially when the features are categorical and one-hot encoding is applied. This makes it more worthwhile mining useful features as w...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	true	false	229,477
1909.08686	Sentiment-Aware Recommendation System for Healthcare using Social Media	Over the last decade, health communities (known as forums) have evolved into platforms where more and more users share their medical experiences, thereby seeking guidance and interacting with people of the community. The shared content, though informal and unstructured in nature, contains valuable medical and/or health...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	146,031
2105.00999	Enhanced U-Net: A Feature Enhancement Network for Polyp Segmentation	Colonoscopy is a procedure to detect colorectal polyps which are the primary cause for developing colorectal cancer. However, polyp segmentation is a challenging task due to the diverse shape, size, color, and texture of polyps, shuttle difference between polyp and its background, as well as low contrast of the colonos...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	233,403
2412.08237	TouchTTS: An Embarrassingly Simple TTS Framework that Everyone Can Touch	It is well known that LLM-based systems are data-hungry. Recent LLM-based TTS works typically employ complex data processing pipelines to obtain high-quality training data. These sophisticated pipelines require excellent models at each stage (e.g., speech denoising, speech enhancement, speaker diarization, and punctuat...	false	false	true	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	516,004
1711.06246	LDMNet: Low Dimensional Manifold Regularized Neural Networks	Deep neural networks have proved very successful on archetypal tasks for which large training sets are available, but when the training data are scarce, their performance suffers from overfitting. Many existing methods of reducing overfitting are data-independent, and their efficacy is often limited when the training s...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	84,732
2301.03561	Ancilia: Scalable Intelligent Video Surveillance for the Artificial Intelligence of Things	With the advancement of vision-based artificial intelligence, the proliferation of the Internet of Things connected cameras, and the increasing societal need for rapid and equitable security, the demand for accurate real-time intelligent surveillance has never been higher. This article presents Ancilia, an end-to-end s...	false	false	false	false	true	false	false	false	false	false	false	true	false	true	false	false	false	false	339,832
1905.07033	Vector Field Neural Networks	This work begins by establishing a mathematical formalization between different geometrical interpretations of Neural Networks, providing a first contribution. From this starting point, a new interpretation is explored, using the idea of implicit vector fields moving data as particles in a flow. A new architecture, Vec...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	131,135
2107.13319	Chance constrained conic-segmentation support vector machine with uncertain data	Support vector machines (SVM) is one of the well known supervised classes of learning algorithms. Furthermore, the conic-segmentation SVM (CS-SVM) is a natural multiclass analogue of the standard binary SVM, as CS-SVM models are dealing with the situation where the exact values of the data points are known. This paper ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	248,167
1609.00951	A Unified Convergence Analysis of the Multiplicative Update Algorithm for Regularized Nonnegative Matrix Factorization	The multiplicative update (MU) algorithm has been extensively used to estimate the basis and coefficient matrices in nonnegative matrix factorization (NMF) problems under a wide range of divergences and regularizers. However, theoretical convergence guarantees have only been derived for a few special divergences withou...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	60,539
2409.13501	HUT: A More Computation Efficient Fine-Tuning Method With Hadamard Updated Transformation	Fine-tuning pre-trained language models for downstream tasks has achieved impressive results in NLP. However, fine-tuning all parameters becomes impractical due to the rapidly increasing size of model parameters. To address this, Parameter Efficient Fine-Tuning (PEFT) methods update only a subset of parameters. Most PE...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	490,021
2010.02926	Learning effective physical laws for generating cosmological hydrodynamics with Lagrangian Deep Learning	The goal of generative models is to learn the intricate relations between the data to create new simulated data, but current approaches fail in very high dimensions. When the true data generating process is based on physical processes these impose symmetries and constraints, and the generative model can be created by l...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	199,212
1802.06305	Machine learning for Internet of Things data analysis: A survey	Rapid developments in hardware, software, and communication technologies have allowed the emergence of Internet-connected sensory devices that provide observation and data measurement from the physical world. By 2020, it is estimated that the total number of Internet-connected devices being used will be between 25 and ...	false	false	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	true	90,640
2109.10759	Astronomical Pipeline Provenance: A Use Case Evaluation	In this decade astronomy is undergoing a paradigm shift to handle data from next generation observatories such as the Square Kilometre Array (SKA) or the Vera C. Rubin Observatory (LSST). Producing real time data streams of up to 10 TB/s and data products of the order of 600 Pbytes/year, the SKA will be the biggest civ...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	256,738
2402.05715	Collaborative non-parametric two-sample testing	This paper addresses the multiple two-sample test problem in a graph-structured setting, which is a common scenario in fields such as Spatial Statistics and Neuroscience. Each node $v$ in fixed graph deals with a two-sample testing problem between two node-specific probability density functions (pdfs), $p_v$ and $q_v$....	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	427,973
2310.17135	Comparison of Cross-Entropy, Dice, and Focal Loss for Sea Ice Type Segmentation	Up-to-date sea ice charts are crucial for safer navigation in ice-infested waters. Recently, Convolutional Neural Network (CNN) models show the potential to accelerate the generation of ice maps for large regions. However, results from CNN models still need to undergo scrutiny as higher metrics performance not always t...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	403,003
1110.3711	Optimization strategies for parallel CPU and GPU implementations of a meshfree particle method	Much of the current focus in high performance computing (HPC) for computational fluid dynamics (CFD) deals with grid based methods. However, parallel implementations for new meshfree particle methods such as Smoothed Particle Hydrodynamics (SPH) are less studied. In this work, we present optimizations for both central ...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	12,688
2502.06649	Estimation of Food Intake Quantity Using Inertial Signals from Smartwatches	Accurate monitoring of eating behavior is crucial for managing obesity and eating disorders such as bulimia nervosa. At the same time, existing methods rely on multiple and/or specialized sensors, greatly harming adherence and ultimately, the quality and continuity of data. This paper introduces a novel approach for es...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	532,167
1909.04824	A Machine Learning Method for Prediction of Multipath Channels	In this paper, a machine learning method for predicting the evolution of a mobile communication channel based on a specific type of convolutional neural network is developed and evaluated in a simulated multipath transmission scenario. The simulation and channel estimation are designed to replicate real-world scenarios...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	144,909
2406.15339	Image Conductor: Precision Control for Interactive Video Synthesis	Filmmaking and animation production often require sophisticated techniques for coordinating camera transitions and object movements, typically involving labor-intensive real-world capturing. Despite advancements in generative AI for video creation, achieving precise control over motion for interactive video asset gener...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	true	466,703
2409.00606	Style Transfer: From Stitching to Neural Networks	This article compares two style transfer methods in image processing: the traditional method, which synthesizes new images by stitching together small patches from existing images, and a modern machine learning-based approach that uses a segmentation network to isolate foreground objects and apply style transfer solely...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	484,993
1807.08381	Pedestrian Trajectory Prediction with Structured Memory Hierarchies	This paper presents a novel framework for human trajectory prediction based on multimodal data (video and radar). Motivated by recent neuroscience discoveries, we propose incorporating a structured memory component in the human trajectory prediction pipeline to capture historical information to improve performance. We ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	103,528
2203.15321	Noise-robust Speech Recognition with 10 Minutes Unparalleled In-domain Data	Noise-robust speech recognition systems require large amounts of training data including noisy speech data and corresponding transcripts to achieve state-of-the-art performances in face of various practical environments. However, such plenty of in-domain data is not always available in the real-life world. In this pape...	false	false	true	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	288,343
2306.01005	AbODE: Ab Initio Antibody Design using Conjoined ODEs	Antibodies are Y-shaped proteins that neutralize pathogens and constitute the core of our adaptive immune system. De novo generation of new antibodies that target specific antigens holds the key to accelerating vaccine discovery. However, this co-design of the amino acid sequence and the 3D structure subsumes and accen...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	370,257
2004.02205	Deep Multimodal Feature Encoding for Video Ordering	True understanding of videos comes from a joint analysis of all its modalities: the video frames, the audio track, and any accompanying text such as closed captions. We present a way to learn a compact multimodal feature representation that encodes all these modalities. Our model parameters are learned through a proxy ...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	true	171,162
cs/0408054	Providing Authentic Long-term Archival Access to Complex Relational Data	We discuss long-term preservation of and access to relational databases. The focus is on national archives and science data archives which have to ingest and integrate data from a broad spectrum of vendor-specific relational database management systems (RDBMS). Furthermore, we present our solution SIARD which analyzes ...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	true	538,313
2106.12658	Transformer-based unsupervised patient representation learning based on medical claims for risk stratification and analysis	The claims data, containing medical codes, services information, and incurred expenditure, can be a good resource for estimating an individual's health condition and medical risk level. In this study, we developed Transformer-based Multimodal AutoEncoder (TMAE), an unsupervised learning framework that can learn efficie...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	242,788
1507.08566	Diffusion Adaptation Over Clustered Multitask Networks Based on the Affine Projection Algorithm	Distributed adaptive networks achieve better estimation performance by exploiting temporal and as well spatial diversity while consuming few resources. Recent works have studied the single task distributed estimation problem, in which the nodes estimate a single optimum parameter vector collaboratively. However, there ...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	true	45,580
2410.05779	LightRAG: Simple and Fast Retrieval-Augmented Generation	Retrieval-Augmented Generation (RAG) systems enhance large language models (LLMs) by integrating external knowledge sources, enabling more accurate and contextually relevant responses tailored to user needs. However, existing RAG systems have significant limitations, including reliance on flat data representations and ...	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	false	495,911
1511.04902	Graph-based denoising for time-varying point clouds	Noisy 3D point clouds arise in many applications. They may be due to errors when constructing a 3D model from images or simply to imprecise depth sensors. Point clouds can be given geometrical structure using graphs created from the similarity information between points. This paper introduces a technique that uses this...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	48,966
2102.01874	Learning to identify image manipulations in scientific publications	Adherence to scientific community standards ensures objectivity, clarity, reproducibility, and helps prevent bias, fabrication, falsification, and plagiarism. To help scientific integrity officers and journal/publisher reviewers monitor if researchers stick with these standards, it is important to have a solid procedur...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	218,247
2108.10826	S&P 500 Stock Price Prediction Using Technical, Fundamental and Text Data	We summarized both common and novel predictive models used for stock price prediction and combined them with technical indices, fundamental characteristics and text-based sentiment data to predict S&P stock prices. A 66.18% accuracy in S&P 500 index directional prediction and 62.09% accuracy in individual stock directi...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	252,020
2203.11891	Evolution is Driven by Natural Autoencoding: Reframing Species, Interaction Codes, Cooperation, and Sexual Reproduction	The continuity of life and its evolution, we proposed, emerge from an interactive group process manifested in networks of interaction. We term this process \textit{survival-of-the-fitted}. Here, we reason that survival of the fitted results from a natural computational process we term \textit{natural autoencoding}. Nat...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	287,076
2406.09356	CMC-Bench: Towards a New Paradigm of Visual Signal Compression	Ultra-low bitrate image compression is a challenging and demanding topic. With the development of Large Multimodal Models (LMMs), a Cross Modality Compression (CMC) paradigm of Image-Text-Image has emerged. Compared with traditional codecs, this semantic-level compression can reduce image data size to 0.1\% or even low...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	463,890
1801.05078	Don't Look Back: Robustifying Place Categorization for Viewpoint- and Condition-Invariant Place Recognition	When a human drives a car along a road for the first time, they later recognize where they are on the return journey typically without needing to look in their rear-view mirror or turn around to look back, despite significant viewpoint and appearance change. Such navigation capabilities are typically attributed to our ...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	88,383
1506.07452	Parallel Multi-Dimensional LSTM, With Application to Fast Biomedical Volumetric Image Segmentation	Convolutional Neural Networks (CNNs) can be shifted across 2D images or 3D videos to segment them. They have a fixed input size and typically perceive only small local contexts of the pixels to be classified as foreground or background. In contrast, Multi-Dimensional Recurrent NNs (MD-RNNs) can perceive the entire spat...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	44,522
2202.11685	A Class of Geometric Structures in Transfer Learning: Minimax Bounds and Optimality	We study the problem of transfer learning, observing that previous efforts to understand its information-theoretic limits do not fully exploit the geometric structure of the source and target domains. In contrast, our study first illustrates the benefits of incorporating a natural geometric structure within a linear re...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	281,956
2003.02093	AI-Mediated Exchange Theory	As Artificial Intelligence (AI) plays an ever-expanding role in sociotechnical systems, it is important to articulate the relationships between humans and AI. However, the scholarly communities studying human-AI relationships -- including but not limited to social computing, machine learning, science and technology stu...	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	166,852
1804.01660	The structure of evolved representations across different substrates for artificial intelligence	Artificial neural networks (ANNs), while exceptionally useful for classification, are vulnerable to misdirection. Small amounts of noise can significantly affect their ability to correctly complete a task. Instead of generalizing concepts, ANNs seem to focus on surface statistical regularities in a given task. Here we ...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	false	false	94,265
2303.05394	A Neurosymbolic Approach to the Verification of Temporal Logic Properties of Learning enabled Control Systems	Signal Temporal Logic (STL) has become a popular tool for expressing formal requirements of Cyber-Physical Systems (CPS). The problem of verifying STL properties of neural network-controlled CPS remains a largely unexplored problem. In this paper, we present a model for the verification of Neural Network (NN) controlle...	false	false	false	false	false	false	true	true	false	false	true	false	false	false	false	false	false	false	350,451
1707.03623	The detector principle of constructing artificial neural networks as an alternative to the connectionist paradigm	Artificial neural networks (ANN) are inadequate to biological neural networks. This inadequacy is manifested in the use of the obsolete model of the neuron and the connectionist paradigm of constructing ANN. The result of this inadequacy is the existence of many shortcomings of the ANN and the problems of their practic...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	76,899
2112.02077	MD-inferred neural network monoclinic finite-strain hyperelasticity models for $\beta$-HMX: Sobolev training and validation against physical constraints	We present a machine learning framework to train and validate neural networks to predict the anisotropic elastic response of the monoclinic organic molecular crystal $\beta$-HMX in the geometrical nonlinear regime. A filtered molecular dynamic (MD) simulations database is used to train the neural networks with a Sobole...	false	true	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	269,719
2202.06590	A Pragmatic Machine Learning Approach to Quantify Tumor Infiltrating Lymphocytes in Whole Slide Images	Increased levels of tumor infiltrating lymphocytes (TILs) in cancer tissue indicate favourable outcomes in many types of cancer. Manual quantification of immune cells is inaccurate and time consuming for pathologists. Our aim is to leverage a computational solution to automatically quantify TILs in whole slide images (...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	280,278
1708.06724	VIGAN: Missing View Imputation with Generative Adversarial Networks	In an era when big data are becoming the norm, there is less concern with the quantity but more with the quality and completeness of the data. In many disciplines, data are collected from heterogeneous sources, resulting in multi-view or multi-modal datasets. The missing data problem has been challenging to address in ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	79,369
2005.03675	Machine Learning on Graphs: A Model and Comprehensive Taxonomy	There has been a surge of recent interest in learning representations for graph-structured data. Graph representation learning methods have generally fallen into three main categories, based on the availability of labeled data. The first, network embedding (such as shallow graph embedding or graph auto-encoders), focus...	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	true	false	false	176,221
1412.4930	Rehabilitation of Count-based Models for Word Vector Representations	Recent works on word representations mostly rely on predictive models. Distributed word representations (aka word embeddings) are trained to optimally predict the contexts in which the corresponding words tend to appear. Such models have succeeded in capturing word similarties as well as semantic and syntactic regulari...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	38,438
2004.01571	Tree-AMP: Compositional Inference with Tree Approximate Message Passing	We introduce Tree-AMP, standing for Tree Approximate Message Passing, a python package for compositional inference in high-dimensional tree-structured models. The package provides a unifying framework to study several approximate message passing algorithms previously derived for a variety of machine learning tasks such...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	170,947
2109.01293	An Open-Source Dataset and A Multi-Task Model for Malay Named Entity Recognition	Named entity recognition (NER) is a fundamental task of natural language processing (NLP). However, most state-of-the-art research is mainly oriented to high-resource languages such as English and has not been widely applied to low-resource languages. In Malay language, relevant NER resources are limited. In this work,...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	253,390
2409.13056	Cross-Chirality Palmprint Verification: Left is Right for the Right Palmprint	Palmprint recognition has emerged as a prominent biometric authentication method, owing to its high discriminative power and user-friendly nature. This paper introduces a novel Cross-Chirality Palmprint Verification (CCPV) framework that challenges the conventional wisdom in traditional palmprint verification systems. ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	489,828
2104.07473	Zooming SlowMo: An Efficient One-Stage Framework for Space-Time Video Super-Resolution	In this paper, we address the space-time video super-resolution, which aims at generating a high-resolution (HR) slow-motion video from a low-resolution (LR) and low frame rate (LFR) video sequence. A na\"ive method is to decompose it into two sub-tasks: video frame interpolation (VFI) and video super-resolution (VSR)....	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	true	230,431
2111.10845	A hybrid optimization approach for employee rostering: Use cases at Swissgrid and lessons learned	Employee rostering is a process of assigning available employees to open shifts. Automating it has ubiquitous practical benefits for nearly all industries, such as reducing manual workload and producing flexible, high-quality schedules. In this work, we develop a hybrid methodology which combines Mixed-Integer Linear P...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	false	false	267,466
2412.01512	ArtBrain: An Explainable end-to-end Toolkit for Classification and Attribution of AI-Generated Art and Style	Recently, the quality of artworks generated using Artificial Intelligence (AI) has increased significantly, resulting in growing difficulties in detecting synthetic artworks. However, limited studies have been conducted on identifying the authenticity of synthetic artworks and their source. This paper introduces AI-Art...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	513,147
2307.02615	Human Inspired Progressive Alignment and Comparative Learning for Grounded Word Acquisition	Human language acquisition is an efficient, supervised, and continual process. In this work, we took inspiration from how human babies acquire their first language, and developed a computational process for word acquisition through comparative learning. Motivated by cognitive findings, we generated a small dataset that...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	377,744
2406.07507	Flow Map Matching	Generative models based on dynamical transport of measure, such as diffusion models, flow matching models, and stochastic interpolants, learn an ordinary or stochastic differential equation whose trajectories push initial conditions from a known base distribution onto the target. While training is cheap, samples are ge...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	463,081
2305.19951	Not All Neuro-Symbolic Concepts Are Created Equal: Analysis and Mitigation of Reasoning Shortcuts	Neuro-Symbolic (NeSy) predictive models hold the promise of improved compliance with given constraints, systematic generalization, and interpretability, as they allow to infer labels that are consistent with some prior knowledge by reasoning over high-level concepts extracted from sub-symbolic inputs. It was recently s...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	369,750
2404.02484	New methods for drug synergy prediction: a mini-review	In this mini-review, we explore the new prediction methods for drug combination synergy relying on high-throughput combinatorial screens. The fast progress of the field is witnessed in the more than thirty original machine learning methods published since 2021, a clear majority of them based on deep learning techniques...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	443,876
2402.05813	Selective Forgetting: Advancing Machine Unlearning Techniques and Evaluation in Language Models	This paper explores Machine Unlearning (MU), an emerging field that is gaining increased attention due to concerns about neural models unintentionally remembering personal or sensitive information. We present SeUL, a novel method that enables selective and fine-grained unlearning for language models. Unlike previous wo...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	428,015
2411.16236	DoubleCCA: Improving Foundation Model Group Robustness with Random Sentence Embeddings	This paper presents a novel method to improve the robustness of foundation models to group-based biases. We propose a simple yet effective method, called DoubleCCA, that leverages random sentences and Canonical Correlation Analysis (CCA) to enrich the text embeddings of the foundation model. First, we generate various ...	false	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	510,956
cs/9903008	Empirically Evaluating an Adaptable Spoken Dialogue System	Recent technological advances have made it possible to build real-time, interactive spoken dialogue systems for a wide variety of applications. However, when users do not respect the limitations of such systems, performance typically degrades. Although users differ with respect to their knowledge of system limitations,...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	540,486
2409.09927	Towards Data Contamination Detection for Modern Large Language Models: Limitations, Inconsistencies, and Oracle Challenges	As large language models achieve increasingly impressive results, questions arise about whether such performance is from generalizability or mere data memorization. Thus, numerous data contamination detection methods have been proposed. However, these approaches are often validated with traditional benchmarks and early...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	488,539
1511.05101	How (not) to Train your Generative Model: Scheduled Sampling, Likelihood, Adversary?	Modern applications and progress in deep learning research have created renewed interest for generative models of text and of images. However, even today it is unclear what objective functions one should use to train and evaluate these models. In this paper we present two contributions. Firstly, we present a critique...	false	false	false	false	true	false	true	false	false	true	false	false	false	false	false	false	false	false	48,993
2406.18576	Negative Prototypes Guided Contrastive Learning for WSOD	Weakly Supervised Object Detection (WSOD) with only image-level annotation has recently attracted wide attention. Many existing methods ignore the inter-image relationship of instances which share similar characteristics while can certainly be determined not to belong to the same category. Therefore, in order to make f...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	468,085
2004.07633	A Methodology for Creating Question Answering Corpora Using Inverse Data Annotation	In this paper, we introduce a novel methodology to efficiently construct a corpus for question answering over structured data. For this, we introduce an intermediate representation that is based on the logical query plan in a database called Operation Trees (OT). This representation allows us to invert the annotation p...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	172,832
2003.00627	Cluster-Based Social Reinforcement Learning	Social Reinforcement Learning methods, which model agents in large networks, are useful for fake news mitigation, personalized teaching/healthcare, and viral marketing, but it is challenging to incorporate inter-agent dependencies into the models effectively due to network size and sparse interaction data. Previous soc...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	166,353
2012.06755	A Meta-Learning Approach for Graph Representation Learning in Multi-Task Settings	Graph Neural Networks (GNNs) are a framework for graph representation learning, where a model learns to generate low dimensional node embeddings that encapsulate structural and feature-related information. GNNs are usually trained in an end-to-end fashion, leading to highly specialized node embeddings. However, generat...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	211,213
2403.05441	Bayesian Hierarchical Probabilistic Forecasting of Intraday Electricity Prices	We address the need for forecasting methodologies that handle large uncertainties in electricity prices for continuous intraday markets by incorporating parameter uncertainty and using a broad set of covariables. This study presents the first Bayesian forecasting of electricity prices traded on the German intraday mark...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	436,009
2106.12074	Reachability Analysis of Convolutional Neural Networks	Deep convolutional neural networks have been widely employed as an effective technique to handle complex and practical problems. However, one of the fundamental problems is the lack of formal methods to analyze their behavior. To address this challenge, we propose an approach to compute the exact reachable sets of a ne...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	242,608
1206.6832	Convex Structure Learning for Bayesian Networks: Polynomial Feature Selection and Approximate Ordering	We present a new approach to learning the structure and parameters of a Bayesian network based on regularized estimation in an exponential family representation. Here we show that, given a fixed variable order, the optimal structure and parameters can be learned efficiently, even without restricting the size of the par...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	17,060
1407.0474	Recent Advances in Joint Wireless Energy and Information Transfer	In this paper, we provide an overview of the recent advances in microwave-enabled wireless energy transfer (WET) technologies and their applications in wireless communications. Specifically, we divide our discussions into three parts. First, we introduce the state-of-the-art WET technologies and the signal processing t...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	34,337
1712.08273	Recurrent Pixel Embedding for Instance Grouping	We introduce a differentiable, end-to-end trainable framework for solving pixel-level grouping problems such as instance segmentation consisting of two novel components. First, we regress pixels into a hyper-spherical embedding space so that pixels from the same group have high cosine similarity while those from differ...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	true	87,165
2106.15590	The Values Encoded in Machine Learning Research	Machine learning currently exerts an outsized influence on the world, increasingly affecting institutional practices and impacted communities. It is therefore critical that we question vague conceptions of the field as value-neutral or universally beneficial, and investigate what specific values the field is advancing....	false	false	false	false	true	false	true	false	false	false	false	false	false	true	false	false	false	false	243,815
2305.02760	Multi-Modality Deep Network for JPEG Artifacts Reduction	In recent years, many convolutional neural network-based models are designed for JPEG artifacts reduction, and have achieved notable progress. However, few methods are suitable for extreme low-bitrate image compression artifacts reduction. The main challenge is that the highly compressed image loses too much informatio...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	362,163
1711.05255	Deep-ESN: A Multiple Projection-encoding Hierarchical Reservoir Computing Framework	As an efficient recurrent neural network (RNN) model, reservoir computing (RC) models, such as Echo State Networks, have attracted widespread attention in the last decade. However, while they have had great success with time series data [1], [2], many time series have a multiscale structure, which a single-hidden-layer...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	84,533
2501.18367	A Learnable Multi-views Contrastive Framework with Reconstruction Discrepancy for Medical Time-Series	In medical time series disease diagnosis, two key challenges are identified.First, the high annotation cost of medical data leads to overfitting in models trained on label-limited, single-center datasets. To address this, we propose incorporating external data from related tasks and leveraging AE-GAN to extract prior k...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	528,675
1712.00557	Recurrent Neural Network Language Models for Open Vocabulary Event-Level Cyber Anomaly Detection	Automated analysis methods are crucial aids for monitoring and defending a network to protect the sensitive or confidential data it hosts. This work introduces a flexible, powerful, and unsupervised approach to detecting anomalous behavior in computer and network logs, one that largely eliminates domain-dependent featu...	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	true	false	false	85,925
2109.07893	Efficient Scaling of Dynamic Graph Neural Networks	We present distributed algorithms for training dynamic Graph Neural Networks (GNN) on large scale graphs spanning multi-node, multi-GPU systems. To the best of our knowledge, this is the first scaling study on dynamic GNN. We devise mechanisms for reducing the GPU memory usage and identify two execution time bottleneck...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	255,693
2205.09676	Beyond Greedy Search: Tracking by Multi-Agent Reinforcement Learning-based Beam Search	To track the target in a video, current visual trackers usually adopt greedy search for target object localization in each frame, that is, the candidate region with the maximum response score will be selected as the tracking result of each frame. However, we found that this may be not an optimal choice, especially when...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	297,372
1602.05281	Generalized Jensen Inequalities with Application to Stability Analysis of Systems with Distributed Delays over Infinite Time-Horizons	The Jensen inequality has been recognized as a powerful tool to deal with the stability of time-delay systems. Recently, a new inequality that encompasses the Jensen inequality was proposed for the stability analysis of systems with finite delays. In this paper, we first present a generalized integral inequality and it...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	52,232
2001.05724	Graph Attentional Autoencoder for Anticancer Hyperfood Prediction	Recent research efforts have shown the possibility to discover anticancer drug-like molecules in food from their effect on protein-protein interaction networks, opening a potential pathway to disease-beating diet design. We formulate this task as a graph classification problem on which graph neural networks (GNNs) have...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	160,622
2301.06901	Graph-based Keyword Planning for Legal Clause Generation from Topics	Generating domain-specific content such as legal clauses based on minimal user-provided information can be of significant benefit in automating legal contract generation. In this paper, we propose a controllable graph-based mechanism that can generate legal clauses using only the topic or type of the legal clauses. Our...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	340,776
2306.17525	MeLM, a generative pretrained language modeling framework that solves forward and inverse mechanics problems	We report a flexible multi-modal mechanics language model, MeLM, applied to solve various nonlinear forward and inverse problems, that can deal with a set of instructions, numbers and microstructure data. The framework is applied to various examples including bio-inspired hierarchical honeycomb design, carbon nanotube ...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	376,731
1905.10961	Fast Convergence of Natural Gradient Descent for Overparameterized Neural Networks	Natural gradient descent has proven effective at mitigating the effects of pathological curvature in neural network optimization, but little is known theoretically about its convergence properties, especially for \emph{nonlinear} networks. In this work, we analyze for the first time the speed of convergence of natural ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	132,278
2405.20400	Fast leave-one-cluster-out cross-validation using clustered Network Information Criterion (NICc)	For prediction models developed on clustered data that do not account for cluster heterogeneity in model parameterization, it is crucial to use cluster-based validation to assess model generalizability on unseen clusters. This paper introduces a clustered estimator of the Network Information Criterion (NICc) to approxi...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	459,324
2401.05421	WildGEN: Long-horizon Trajectory Generation for Wildlife	Trajectory generation is an important concern in pedestrian, vehicle, and wildlife movement studies. Generated trajectories help enrich the training corpus in relation to deep learning applications, and may be used to facilitate simulation tasks. This is especially significant in the wildlife domain, where the cost of ...	false	false	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	420,776
2312.07161	One-dimensional Convolutional Neural Networks for Detecting Transiting Exoplanets	The transit method is one of the most relevant exoplanet detection techniques, which consists of detecting periodic eclipses in the light curves of stars. This is not always easy due to the presence of noise in the light curves, which is induced, for example, by the response of a telescope to stellar flux. For this rea...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	414,820
2210.08933	DiffuSeq: Sequence to Sequence Text Generation with Diffusion Models	Recently, diffusion models have emerged as a new paradigm for generative models. Despite the success in domains using continuous signals such as vision and audio, adapting diffusion models to natural language is under-explored due to the discrete nature of texts, especially for conditional generation. We tackle this ch...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	324,356
1310.4891	Dictionary Learning and Sparse Coding on Grassmann Manifolds: An Extrinsic Solution	Recent advances in computer vision and machine learning suggest that a wide range of problems can be addressed more appropriately by considering non-Euclidean geometry. In this paper we explore sparse dictionary learning over the space of linear subspaces, which form Riemannian structures known as Grassmann manifolds. ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	27,844
2204.01593	Optimize Deep Learning Models for Prediction of Gene Mutations Using Unsupervised Clustering	Deep learning has become the mainstream methodological choice for analyzing and interpreting whole-slide digital pathology images (WSIs). It is commonly assumed that tumor regions carry most predictive information. In this paper, we proposed an unsupervised clustering-based multiple-instance learning, and apply our met...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	289,653
2403.02902	Demonstrating Mutual Reinforcement Effect through Information Flow	The Mutual Reinforcement Effect (MRE) investigates the synergistic relationship between word-level and text-level classifications in text classification tasks. It posits that the performance of both classification levels can be mutually enhanced. However, this mechanism has not been adequately demonstrated or explained...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	434,985
1003.5993	A Triple-Error-Correcting Cyclic Code from the Gold and Kasami-Welch APN Power Functions	Based on a sufficient condition proposed by Hollmann and Xiang for constructing triple-error-correcting codes, the minimum distance of a binary cyclic code $\mathcal{C}_{1,3,13}$ with three zeros $\alpha$, $\alpha^3$, and $\alpha^{13}$ of length $2^m-1$ and the weight divisibility of its dual code are studied, where $m...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	6,044
2201.06172	On the c-differential spectrum of power functions over finite fields	Recently, a new concept called multiplicative differential was introduced by Ellingsen et al. Inspired by this pioneering work, power functions with low c-differential uniformity were constructed. Wang et al. defined the c-differential spectrum of a power function [27]. In this paper, we present some properties of the ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	275,634
2306.04997	Blockage Prediction in Directional mmWave Links Using Liquid Time Constant Network	We propose to use a liquid time constant (LTC) network to predict the future blockage status of a millimeter wave (mmWave) link using only the received signal power as the input to the system. The LTC network is based on an ordinary differential equation (ODE) system inspired by biology and specialized for near-future ...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	372,007

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