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
2004.06716	Does purely physical information have meaning? A comment on Carlo Rovelli's paper: Meaning = information + evolution [arXiv:1611.02420]	The note discusses the concept of meaningful, physical information presented by Carlo Rovelli. It points out certain consequences of the information model not elucidated in the original paper but important to its comprehensive understanding.	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	172,585
2208.10240	A Multimodal Transformer: Fusing Clinical Notes with Structured EHR Data for Interpretable In-Hospital Mortality Prediction	Deep-learning-based clinical decision support using structured electronic health records (EHR) has been an active research area for predicting risks of mortality and diseases. Meanwhile, large amounts of narrative clinical notes provide complementary information, but are often not integrated into predictive models. In ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	313,979
2311.16700	Rethinking Intermediate Layers design in Knowledge Distillation for Kidney and Liver Tumor Segmentation	Knowledge distillation (KD) has demonstrated remarkable success across various domains, but its application to medical imaging tasks, such as kidney and liver tumor segmentation, has encountered challenges. Many existing KD methods are not specifically tailored for these tasks. Moreover, prevalent KD methods often lack...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	411,019
2402.16991	A Phase Transition in Diffusion Models Reveals the Hierarchical Nature of Data	Understanding the structure of real data is paramount in advancing modern deep-learning methodologies. Natural data such as images are believed to be composed of features organized in a hierarchical and combinatorial manner, which neural networks capture during learning. Recent advancements show that diffusion models c...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	432,793
1302.1334	Principles of modal and vector theory of formal intelligence systems	The paper considers the class of information systems capable of solving heuristic problems on basis of formal theory that was termed modal and vector theory of formal intelligent systems (FIS). The paper justifies the construction of FIS resolution algorithm, defines the main features of these systems and proves theore...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	21,800
2402.00654	Improving the accuracy of freight mode choice models: A case study using the 2017 CFS PUF data set and ensemble learning techniques	The US Census Bureau has collected two rounds of experimental data from the Commodity Flow Survey, providing shipment-level characteristics of nationwide commodity movements, published in 2012 (i.e., Public Use Microdata) and in 2017 (i.e., Public Use File). With this information, data-driven methods have become increa...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	425,669
2302.05007	Scalability Bottlenecks in Multi-Agent Reinforcement Learning Systems	Multi-Agent Reinforcement Learning (MARL) is a promising area of research that can model and control multiple, autonomous decision-making agents. During online training, MARL algorithms involve performance-intensive computations such as exploration and exploitation phases originating from large observation-action space...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	344,893
2403.16875	TAIL: A Terrain-Aware Multi-Modal SLAM Dataset for Robot Locomotion in Deformable Granular Environments	Terrain-aware perception holds the potential to improve the robustness and accuracy of autonomous robot navigation in the wilds, thereby facilitating effective off-road traversals. However, the lack of multi-modal perception across various motion patterns hinders the solutions of Simultaneous Localization And Mapping (...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	441,227
2310.08780	"Im not Racist but...": Discovering Bias in the Internal Knowledge of Large Language Models	Large language models (LLMs) have garnered significant attention for their remarkable performance in a continuously expanding set of natural language processing tasks. However, these models have been shown to harbor inherent societal biases, or stereotypes, which can adversely affect their performance in their many dow...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	399,528
2401.01459	Outlier Ranking in Large-Scale Public Health Streams	Disease control experts inspect public health data streams daily for outliers worth investigating, like those corresponding to data quality issues or disease outbreaks. However, they can only examine a few of the thousands of maximally-tied outliers returned by univariate outlier detection methods applied to large-scal...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	419,360
1907.03355	Improving Detection of Credit Card Fraudulent Transactions using Generative Adversarial Networks	In this study, we employ Generative Adversarial Networks as an oversampling method to generate artificial data to assist with the classification of credit card fraudulent transactions. GANs is a generative model based on the idea of game theory, in which a generator G and a discriminator D are trying to outsmart each o...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	137,842
1612.03900	Deep Supervised Hashing with Triplet Labels	Hashing is one of the most popular and powerful approximate nearest neighbor search techniques for large-scale image retrieval. Most traditional hashing methods first represent images as off-the-shelf visual features and then produce hashing codes in a separate stage. However, off-the-shelf visual features may not be o...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	65,438
2209.15565	Augmenting Operations Research with Auto-Formulation of Optimization Models from Problem Descriptions	We describe an augmented intelligence system for simplifying and enhancing the modeling experience for operations research. Using this system, the user receives a suggested formulation of an optimization problem based on its description. To facilitate this process, we build an intuitive user interface system that enabl...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	320,650
2204.06918	SoccerNet-Tracking: Multiple Object Tracking Dataset and Benchmark in Soccer Videos	Tracking objects in soccer videos is extremely important to gather both player and team statistics, whether it is to estimate the total distance run, the ball possession or the team formation. Video processing can help automating the extraction of those information, without the need of any invasive sensor, hence applic...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	291,493
2307.04106	Parametric Depth Based Feature Representation Learning for Object Detection and Segmentation in Bird's Eye View	Recent vision-only perception models for autonomous driving achieved promising results by encoding multi-view image features into Bird's-Eye-View (BEV) space. A critical step and the main bottleneck of these methods is transforming image features into the BEV coordinate frame. This paper focuses on leveraging geometry ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	378,290
1604.05819	Trading-Off Cost of Deployment Versus Accuracy in Learning Predictive Models	Predictive models are finding an increasing number of applications in many industries. As a result, a practical means for trading-off the cost of deploying a model versus its effectiveness is needed. Our work is motivated by risk prediction problems in healthcare. Cost-structures in domains such as healthcare are quite...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	54,867
2002.03095	Attacking Optical Character Recognition (OCR) Systems with Adversarial Watermarks	Optical character recognition (OCR) is widely applied in real applications serving as a key preprocessing tool. The adoption of deep neural network (DNN) in OCR results in the vulnerability against adversarial examples which are crafted to mislead the output of the threat model. Different from vanilla colorful images, ...	false	false	false	false	false	false	true	false	false	false	false	true	true	false	false	false	false	false	163,133
2302.03338	Learning Manner of Execution from Partial Corrections	Some actions must be executed in different ways depending on the context. For example, wiping away marker requires vigorous force while wiping away almonds requires more gentle force. In this paper we provide a model where an agent learns which manner of action execution to use in which context, drawing on evidence fro...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	344,306
1805.12067	A Robust and Effective Approach Towards Accurate Metastasis Detection and pN-stage Classification in Breast Cancer	Predicting TNM stage is the major determinant of breast cancer prognosis and treatment. The essential part of TNM stage classification is whether the cancer has metastasized to the regional lymph nodes (N-stage). Pathologic N-stage (pN-stage) is commonly performed by pathologists detecting metastasis in histological sl...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	99,085
2007.06835	Programming by Rewards	We formalize and study ``programming by rewards'' (PBR), a new approach for specifying and synthesizing subroutines for optimizing some quantitative metric such as performance, resource utilization, or correctness over a benchmark. A PBR specification consists of (1) input features $x$, and (2) a reward function $r$, m...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	true	187,143
1306.2700	Hierarchical Interference Mitigation for Massive MIMO Cellular Networks	We propose a hierarchical interference mitigation scheme for massive MIMO cellular networks. The MIMO precoder at each base station (BS) is partitioned into an inner precoder and an outer precoder. The inner precoder controls the intra-cell interference and is adaptive to local channel state information (CSI) at each B...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	25,149
2102.03974	Novel Deep neural networks for solving Bayesian statistical inverse	We consider the simulation of Bayesian statistical inverse problems governed by large-scale linear and nonlinear partial differential equations (PDEs). Markov chain Monte Carlo (MCMC) algorithms are standard techniques to solve such problems. However, MCMC techniques are computationally challenging as they require seve...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	218,939
2002.11656	Inceptive Event Time-Surfaces for Object Classification Using Neuromorphic Cameras	This paper presents a novel fusion of low-level approaches for dimensionality reduction into an effective approach for high-level objects in neuromorphic camera data called Inceptive Event Time-Surfaces (IETS). IETSs overcome several limitations of conventional time-surfaces by increasing robustness to noise, promoting...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	true	false	false	165,790
2301.02731	Attention-LSTM for Multivariate Traffic State Prediction on Rural Roads	Accurate traffic volume and speed prediction have a wide range of applications in transportation. It can result in useful and timely information for both travellers and transportation decision-makers. In this study, an Attention based Long Sort-Term Memory model (A-LSTM) is proposed to simultaneously predict traffic vo...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	339,574
1602.05568	Multi-layer Representation Learning for Medical Concepts	Learning efficient representations for concepts has been proven to be an important basis for many applications such as machine translation or document classification. Proper representations of medical concepts such as diagnosis, medication, procedure codes and visits will have broad applications in healthcare analytics...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	52,274
2410.20788	SCULPT: Systematic Tuning of Long Prompts	As large language models become increasingly central to solving complex tasks, the challenge of optimizing long, unstructured prompts has become critical. Existing optimization techniques often struggle to effectively handle such prompts, leading to suboptimal performance. We introduce SCULPT (Systematic Tuning of Long...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	502,965
2205.11232	Deep Neural Network approaches for Analysing Videos of Music Performances	This paper presents a framework to automate the labelling process for gestures in musical performance videos with a 3D Convolutional Neural Network (CNN). While this idea was proposed in a previous study, this paper introduces several novelties: (i) Presents a novel method to overcome the class imbalance challenge and ...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	true	298,058
2402.09460	Unsupervised learning based end-to-end delayless generative fixed-filter active noise control	Delayless noise control is achieved by our earlier generative fixed-filter active noise control (GFANC) framework through efficient coordination between the co-processor and real-time controller. However, the one-dimensional convolutional neural network (1D CNN) in the co-processor requires initial training using label...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	429,533
1906.11617	A non-intrusive reduced order modeling framework for quasi-geostrophic turbulence	In this study, we present a non-intrusive reduced order modeling (ROM) framework for large-scale quasi-stationary systems. The framework proposed herein exploits the time series prediction capability of long short-term memory (LSTM) recurrent neural network such that: (i) in the training phase, the LSTM model is traine...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	136,709
2410.23934	Towards Fast Algorithms for the Preference Consistency Problem Based on Hierarchical Models	In this paper, we construct and compare algorithmic approaches to solve the Preference Consistency Problem for preference statements based on hierarchical models. Instances of this problem contain a set of preference statements that are direct comparisons (strict and non-strict) between some alternatives, and a set of ...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	504,255
2103.05672	Entropy-Guided Control Improvisation	High level declarative constraints provide a powerful (and popular) way to define and construct control policies; however, most synthesis algorithms do not support specifying the degree of randomness (unpredictability) of the resulting controller. In many contexts, e.g., patrolling, testing, behavior prediction,and pla...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	true	224,047
1512.07685	Service Choreography, SBVR, and Time	We propose the use of structured natural language (English) in specifying service choreographies, focusing on the what rather than the how of the required coordination of participant services in realising a business application scenario. The declarative approach we propose uses the OMG standard Semantics of Business Vo...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	50,437
1812.02827	Complementarity Assessment of South Greenland Katabatic Flows and West Europe Wind Regimes	Current global environmental challenges require vigorous and diverse actions in the energy sector. One solution that has recently attracted interest consists in harnessing high-quality variable renewable energy resources in remote locations, while using transmission links to transport the power to end users. In this co...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	115,860
2106.05763	A Deep Variational Approach to Clustering Survival Data	In this work, we study the problem of clustering survival data $-$ a challenging and so far under-explored task. We introduce a novel semi-supervised probabilistic approach to cluster survival data by leveraging recent advances in stochastic gradient variational inference. In contrast to previous work, our proposed met...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	240,218
2305.17209	Functional Flow Matching	We propose Functional Flow Matching (FFM), a function-space generative model that generalizes the recently-introduced Flow Matching model to operate in infinite-dimensional spaces. Our approach works by first defining a path of probability measures that interpolates between a fixed Gaussian measure and the data distrib...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	368,463
0909.4474	Reconstruction of the equilibrium of the plasma in a Tokamak and identification of the current density profile in real time	The reconstruction of the equilibrium of a plasma in a Tokamak is a free boundary problem described by the Grad-Shafranov equation in axisymmetric configuration. The right-hand side of this equation is a nonlinear source, which represents the toroidal component of the plasma current density. This paper deals with the i...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	4,562
2406.13632	Can Few-shot Work in Long-Context? Recycling the Context to Generate Demonstrations	Despite recent advancements in Large Language Models (LLMs), their performance on tasks involving long contexts remains sub-optimal. In-Context Learning (ICL) with few-shot examples may be an appealing solution to enhance LLM performance in this scenario; However, na\"ively adding ICL examples with long context introdu...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	465,928
1710.03856	Attack Analysis for Distributed Control Systems: An Internal Model Principle Approach	Although adverse effects of attacks have been acknowledged in many cyber-physical systems, there is no system-theoretic comprehension of how a compromised agent can leverage communication capabilities to maximize the damage in distributed multi-agent systems. A rigorous analysis of cyber-physical attacks enables us to ...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	82,382
1908.05256	Continuous Control for High-Dimensional State Spaces: An Interactive Learning Approach	Deep Reinforcement Learning (DRL) has become a powerful methodology to solve complex decision-making problems. However, DRL has several limitations when used in real-world problems (e.g., robotics applications). For instance, long training times are required and cannot be accelerated in contrast to simulated environmen...	false	false	false	false	true	false	true	true	false	false	true	false	false	false	false	false	false	false	141,675
2406.04116	Promoting the Responsible Development of Speech Datasets for Mental Health and Neurological Disorders Research	Current research in machine learning and artificial intelligence is largely centered on modeling and performance evaluation, less so on data collection. However, recent research demonstrated that limitations and biases in data may negatively impact trustworthiness and reliability. These aspects are particularly impactf...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	461,529
2304.12686	On the Computation of Meaning, Language Models and Incomprehensible Horrors	We integrate foundational theories of meaning with a mathematical formalism of artificial general intelligence (AGI) to offer a comprehensive mechanistic explanation of meaning, communication, and symbol emergence. This synthesis holds significance for both AGI and broader debates concerning the nature of language, as ...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	360,320
2103.00497	Distilling Knowledge via Intermediate Classifiers	The crux of knowledge distillation is to effectively train a resource-limited student model with the guide of a pre-trained larger teacher model. However, when there is a large difference between the model complexities of teacher and student (i.e., capacity gap), knowledge distillation loses its strength in transferrin...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	222,304
2308.10794	MGMAE: Motion Guided Masking for Video Masked Autoencoding	Masked autoencoding has shown excellent performance on self-supervised video representation learning. Temporal redundancy has led to a high masking ratio and customized masking strategy in VideoMAE. In this paper, we aim to further improve the performance of video masked autoencoding by introducing a motion guided mask...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	386,884
2410.13461	Progressive Mixed-Precision Decoding for Efficient LLM Inference	In spite of the great potential of large language models (LLMs) across various tasks, their deployment on resource-constrained devices remains challenging due to their excessive computational and memory demands. Quantization has emerged as an effective solution by storing weights in reduced precision. However, utilizin...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	499,544
2406.11935	A Problem-Oriented Perspective and Anchor Verification for Code Optimization	Large language models (LLMs) have shown remarkable capabilities in solving various programming tasks, such as code generation. However, their potential for code optimization, particularly in performance enhancement, remains largely unexplored. This paper investigates the capabilities of LLMs in optimizing code for mini...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	465,157
2209.08422	Computed Decision Weights and a New Learning Algorithm for Neural Classifiers	In this paper we consider the possibility of computing rather than training the decision layer weights of a neural classifier. Such a possibility arises in two way, from making an appropriate choice of loss function and by solving a problem of constrained optimization. The latter formulation leads to a promising new le...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	318,118
2207.13238	Trajectory Planning of Cellular-Connected UAV for Communication-assisted Radar Sensing	Being a key technology for beyond fifth-generation wireless systems, joint communication and radar sensing (JCAS) utilizes the reflections of communication signals to detect foreign objects and deliver situational awareness. A cellular-connected unmanned aerial vehicle (UAV) is uniquely suited to form a mobile bistatic...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	310,234
2004.12275	Citation Cascade and the Evolution of Topic Relevance	Citation analysis, as a tool for quantitative studies of science, has long emphasized direct citation relations, leaving indirect or high order citations overlooked. However, a series of early and recent studies demonstrate the existence of indirect and continuous citation impact across generations. Adding to the liter...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	true	174,191
2309.09464	Reducing Adversarial Training Cost with Gradient Approximation	Deep learning models have achieved state-of-the-art performances in various domains, while they are vulnerable to the inputs with well-crafted but small perturbations, which are named after adversarial examples (AEs). Among many strategies to improve the model robustness against AEs, Projected Gradient Descent (PGD) ba...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	392,618
1304.1972	Facial transformations of ancient portraits: the face of Caesar	Some software solutions used to obtain the facial transformations can help investigating the artistic metamorphosis of the ancient portraits of the same person. An analysis with a freely available software of portraitures of Julius Caesar is proposed, showing his several "morphs". The software helps enhancing the mood ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	23,612
2301.07896	Supercharging Distributed Computing Environments For High Performance Data Engineering	The data engineering and data science community has embraced the idea of using Python & R dataframes for regular applications. Driven by the big data revolution and artificial intelligence, these applications are now essential in order to process terabytes of data. They can easily exceed the capabilities of a single ma...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	true	341,040
2306.04176	When to Read Documents or QA History: On Unified and Selective Open-domain QA	This paper studies the problem of open-domain question answering, with the aim of answering a diverse range of questions leveraging knowledge resources. Two types of sources, QA-pair and document corpora, have been actively leveraged with the following complementary strength. The former is highly precise when the parap...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	371,636
2402.18821	Debiased Novel Category Discovering and Localization	In recent years, object detection in deep learning has experienced rapid development. However, most existing object detection models perform well only on closed-set datasets, ignoring a large number of potential objects whose categories are not defined in the training set. These objects are often identified as backgrou...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	433,575
2211.12512	NLP meets psychotherapy: Using predicted client emotions and self-reported client emotions to measure emotional coherence	Emotions are experienced and expressed through various response systems. Coherence between emotional experience and emotional expression is considered important to clients' well being. To date, emotional coherence (EC) has been studied at a single time point using lab-based tasks with relatively small datasets. No stud...	true	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	332,141
2212.01578	High-Speed Resource Allocation Algorithm Using a Coherent Ising Machine for NOMA Systems	Non-orthogonal multiple access (NOMA) technique is important for achieving a high data rate in next-generation wireless communications. A key challenge to fully utilizing the effectiveness of the NOMA technique is the optimization of the resource allocation (RA), e.g., channel and power. However, this RA optimization p...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	334,488
2407.17265	SCIsegV2: A Universal Tool for Segmentation of Intramedullary Lesions in Spinal Cord Injury	Spinal cord injury (SCI) is a devastating incidence leading to permanent paralysis and loss of sensory-motor functions potentially resulting in the formation of lesions within the spinal cord. Imaging biomarkers obtained from magnetic resonance imaging (MRI) scans can predict the functional recovery of individuals with...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	475,903
2501.11236	A New Formulation of Lipschitz Constrained With Functional Gradient Learning for GANs	This paper introduces a promising alternative method for training Generative Adversarial Networks (GANs) on large-scale datasets with clear theoretical guarantees. GANs are typically learned through a minimax game between a generator and a discriminator, which is known to be empirically unstable. Previous learning para...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	525,845
2502.14819	Learning from Reward-Free Offline Data: A Case for Planning with Latent Dynamics Models	A long-standing goal in AI is to build agents that can solve a variety of tasks across different environments, including previously unseen ones. Two dominant approaches tackle this challenge: (i) reinforcement learning (RL), which learns policies through trial and error, and (ii) optimal control, which plans actions us...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	535,994
1403.5521	Scenario optimization with certificates and applications to anti-windup design	In this paper, we introduce a significant extension, called scenario with certificates (SwC), of the so-called scenario approach for uncertain optimization problems. This extension is motivated by the observation that in many control problems only some of the optimization variables are used in the design phase, while t...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	31,735
2402.05264	AdaBatchGrad: Combining Adaptive Batch Size and Adaptive Step Size	This paper presents a novel adaptation of the Stochastic Gradient Descent (SGD), termed AdaBatchGrad. This modification seamlessly integrates an adaptive step size with an adjustable batch size. An increase in batch size and a decrease in step size are well-known techniques to tighten the area of convergence of SGD and...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	427,789
2308.02869	Semi-supervised Learning for Segmentation of Bleeding Regions in Video Capsule Endoscopy	In the realm of modern diagnostic technology, video capsule endoscopy (VCE) is a standout for its high efficacy and non-invasive nature in diagnosing various gastrointestinal (GI) conditions, including obscure bleeding. Importantly, for the successful diagnosis and treatment of these conditions, accurate recognition of...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	383,799
1610.01891	A New Data Representation Based on Training Data Characteristics to Extract Drug Named-Entity in Medical Text	One essential task in information extraction from the medical corpus is drug name recognition. Compared with text sources come from other domains, the medical text is special and has unique characteristics. In addition, the medical text mining poses more challenges, e.g., more unstructured text, the fast growing of new...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	true	false	false	62,022
1706.04388	Alignment Distances on Systems of Bags	Recent research in image and video recognition indicates that many visual processes can be thought of as being generated by a time-varying generative model. A nearby descriptive model for visual processes is thus a statistical distribution that varies over time. Specifically, modeling visual processes as streams of his...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	75,336
1704.05203	ECG Signal Compression and Optimization in Remote Monitoring Networks	We proposed a practical ECG compression system which is beneficial for tele-monitoring cardiovascular diseases. There are two steps in the compression framework. First, we partition ECG signal into segments according to R- to R-wave periods. The partition aims at achieving more stable statistical features between segme...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	71,965
2501.14056	Prior Knowledge Injection into Deep Learning Models Predicting Gene Expression from Whole Slide Images	Cancer diagnosis and prognosis primarily depend on clinical parameters such as age and tumor grade, and are increasingly complemented by molecular data, such as gene expression, from tumor sequencing. However, sequencing is costly and delays oncology workflows. Recent advances in Deep Learning allow to predict molecula...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	526,962
2207.04812	A clinically motivated self-supervised approach for content-based image retrieval of CT liver images	Deep learning-based approaches for content-based image retrieval (CBIR) of CT liver images is an active field of research, but suffers from some critical limitations. First, they are heavily reliant on labeled data, which can be challenging and costly to acquire. Second, they lack transparency and explainability, which...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	307,320
1904.04297	Learned 3D Shape Representations Using Fused Geometrically Augmented Images: Application to Facial Expression and Action Unit Detection	This paper proposes an approach to learn generic multi-modal mesh surface representations using a novel scheme for fusing texture and geometric data. Our approach defines an inverse mapping between different geometric descriptors computed on the mesh surface or its down-sampled version, and the corresponding 2D texture...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	126,991
2311.15637	Neural 3D Strokes: Creating Stylized 3D Scenes with Vectorized 3D Strokes	We present Neural 3D Strokes, a novel technique to generate stylized images of a 3D scene at arbitrary novel views from multi-view 2D images. Different from existing methods which apply stylization to trained neural radiance fields at the voxel level, our approach draws inspiration from image-to-painting methods, simul...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	410,592
2501.18012	When less is more: evolving large neural networks from small ones	In contrast to conventional artificial neural networks, which are large and structurally static, we study feed-forward neural networks that are small and dynamic, whose nodes can be added (or subtracted) during training. A single neuronal weight in the network controls the network's size, while the weight itself is opt...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	528,536
2207.08159	Task-aware Similarity Learning for Event-triggered Time Series	Time series analysis has achieved great success in diverse applications such as network security, environmental monitoring, and medical informatics. Learning similarities among different time series is a crucial problem since it serves as the foundation for downstream analysis such as clustering and anomaly detection. ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	308,478
2409.08596	Large Language Model Can Transcribe Speech in Multi-Talker Scenarios with Versatile Instructions	Recent advancements in large language models (LLMs) have revolutionized various domains, bringing significant progress and new opportunities. Despite progress in speech-related tasks, LLMs have not been sufficiently explored in multi-talker scenarios. In this work, we present a pioneering effort to investigate the capa...	false	false	true	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	487,977
2105.07112	NeuLF: Efficient Novel View Synthesis with Neural 4D Light Field	In this paper, we present an efficient and robust deep learning solution for novel view synthesis of complex scenes. In our approach, a 3D scene is represented as a light field, i.e., a set of rays, each of which has a corresponding color when reaching the image plane. For efficient novel view rendering, we adopt a two...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	235,320
2407.01715	Generation Expansion Equilibria with Predictive Dispatch Model	This paper proposes a methodology to solve generation expansion equilibrium problems by using a predictive model to represent the equilibrium in a simplified network constrained electricity market. The investment problem for each generation company (Genco) is a bi-level problem with the investment decision made in the ...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	469,422
2406.14208	SeCoKD: Aligning Large Language Models for In-Context Learning with Fewer Shots	Previous studies have shown that demonstrations can significantly help Large Language Models (LLMs ) perform better on the given tasks. However, this so-called In-Context Learning ( ICL ) ability is very sensitive to the presenting context, and often dozens of demonstrations are needed. In this work, we investigate if ...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	466,206
1601.06931	Fisher Motion Descriptor for Multiview Gait Recognition	The goal of this paper is to identify individuals by analyzing their gait. Instead of using binary silhouettes as input data (as done in many previous works) we propose and evaluate the use of motion descriptors based on densely sampled short-term trajectories. We take advantage of state-of-the-art people detectors to ...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	51,362
1307.1718	Graph-based Approach to Automatic Taxonomy Generation (GraBTax)	We propose a novel graph-based approach for constructing concept hierarchy from a large text corpus. Our algorithm, GraBTax, incorporates both statistical co-occurrences and lexical similarity in optimizing the structure of the taxonomy. To automatically generate topic-dependent taxonomies from a large text corpus, Gra...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	25,655
1906.04113	BlockSwap: Fisher-guided Block Substitution for Network Compression on a Budget	The desire to map neural networks to varying-capacity devices has led to the development of a wealth of compression techniques, many of which involve replacing standard convolutional blocks in a large network with cheap alternative blocks. However, not all blocks are created equally; for a required compute budget there...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	134,608
2402.00794	ReAGent: A Model-agnostic Feature Attribution Method for Generative Language Models	Feature attribution methods (FAs), such as gradients and attention, are widely employed approaches to derive the importance of all input features to the model predictions. Existing work in natural language processing has mostly focused on developing and testing FAs for encoder-only language models (LMs) in classificati...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	425,726
2008.11689	5G Utility Pole Planner Using Google Street View and Mask R-CNN	With the advances of fifth-generation (5G) cellular networks technology, many studies and work have been carried out on how to build 5G networks for smart cities. In the previous research, street lighting poles and smart light poles are capable of being a 5G access point. In order to determine the position of the point...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	193,350
2402.13446	Large Language Models for Data Annotation and Synthesis: A Survey	Data annotation and synthesis generally refers to the labeling or generating of raw data with relevant information, which could be used for improving the efficacy of machine learning models. The process, however, is labor-intensive and costly. The emergence of advanced Large Language Models (LLMs), exemplified by GPT-4...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	431,248
2307.03918	VS-TransGRU: A Novel Transformer-GRU-based Framework Enhanced by Visual-Semantic Fusion for Egocentric Action Anticipation	Egocentric action anticipation is a challenging task that aims to make advanced predictions of future actions from current and historical observations in the first-person view. Most existing methods focus on improving the model architecture and loss function based on the visual input and recurrent neural network to boo...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	378,207
2010.09941	Multiple-view clustering for identifying subject clusters and brain sub-networks using functional connectivity matrices without vectorization	In neuroscience, the functional magnetic resonance imaging (fMRI) is a vital tool to non-invasively access brain activity. Using fMRI, the functional connectivity (FC) between brain regions can be inferred, which has contributed to a number of findings of the fundamental properties of the brain. As an important clinica...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	201,718
1810.06807	Morph: Flexible Acceleration for 3D CNN-based Video Understanding	The past several years have seen both an explosion in the use of Convolutional Neural Networks (CNNs) and the design of accelerators to make CNN inference practical. In the architecture community, the lion share of effort has targeted CNN inference for image recognition. The closely related problem of video recognition...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	true	110,511
2406.07424	MINERS: Multilingual Language Models as Semantic Retrievers	Words have been represented in a high-dimensional vector space that encodes their semantic similarities, enabling downstream applications such as retrieving synonyms, antonyms, and relevant contexts. However, despite recent advances in multilingual language models (LMs), the effectiveness of these models' representatio...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	463,041
1904.05200	Active Multi-Kernel Domain Adaptation for Hyperspectral Image Classification	Recent years have witnessed the quick progress of the hyperspectral images (HSI) classification. Most of existing studies either heavily rely on the expensive label information using the supervised learning or can hardly exploit the discriminative information borrowed from related domains. To address this issues, in th...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	127,236
2301.11997	Prompt-Based Editing for Text Style Transfer	Prompting approaches have been recently explored in text style transfer, where a textual prompt is used to query a pretrained language model to generate style-transferred texts word by word in an autoregressive manner. However, such a generation process is less controllable and early prediction errors may affect future...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	342,343
2011.09634	Watch and Learn: Mapping Language and Noisy Real-world Videos with Self-supervision	In this paper, we teach machines to understand visuals and natural language by learning the mapping between sentences and noisy video snippets without explicit annotations. Firstly, we define a self-supervised learning framework that captures the cross-modal information. A novel adversarial learning module is then intr...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	207,249
2411.10458	Neural decoding from stereotactic EEG: accounting for electrode variability across subjects	Deep learning based neural decoding from stereotactic electroencephalography (sEEG) would likely benefit from scaling up both dataset and model size. To achieve this, combining data across multiple subjects is crucial. However, in sEEG cohorts, each subject has a variable number of electrodes placed at distinct locatio...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	508,643
2007.02492	Searching Scientific Literature for Answers on COVID-19 Questions	Finding answers related to a pandemic of a novel disease raises new challenges for information seeking and retrieval, as the new information becomes available gradually. TREC COVID search track aims to assist in creating search tools to aid scientists, clinicians, policy makers and others with similar information needs...	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	185,765
2311.03370	CMIP X-MOS: Improving Climate Models with Extreme Model Output Statistics	Climate models are essential for assessing the impact of greenhouse gas emissions on our changing climate and the resulting increase in the frequency and severity of natural disasters. Despite the widespread acceptance of climate models produced by the Coupled Model Intercomparison Project (CMIP), they still face chall...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	405,821
2204.08263	Factual Error Correction for Abstractive Summaries Using Entity Retrieval	Despite the recent advancements in abstractive summarization systems leveraged from large-scale datasets and pre-trained language models, the factual correctness of the summary is still insufficient. One line of trials to mitigate this problem is to include a post-editing process that can detect and correct factual err...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	292,022
1206.1953	Improvement of Loadability in Distribution System Using Genetic Algorithm	Generally during recent decades due to development of power systems, the methods for delivering electrical energy to consumers, and because of voltage variations is a very important problem, the power plants follow this criteria. The good solution for improving transfer and distribution of electrical power the majority...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	true	false	false	16,406
1908.08631	Image based cellular contractile force evaluation with small-world network inspired CNN: SW-UNet	We propose an image-based cellular contractile force evaluation method using a machine learning technique. We use a special substrate that exhibits wrinkles when cells grab the substrate and contract, and the wrinkles can be used to visualize the force magnitude and direction. In order to extract wrinkles from the micr...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	142,606
2404.19758	Invisible Stitch: Generating Smooth 3D Scenes with Depth Inpainting	3D scene generation has quickly become a challenging new research direction, fueled by consistent improvements of 2D generative diffusion models. Most prior work in this area generates scenes by iteratively stitching newly generated frames with existing geometry. These works often depend on pre-trained monocular depth ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	450,763
2403.07013	AdaNovo: Adaptive \emph{De Novo} Peptide Sequencing with Conditional Mutual Information	Tandem mass spectrometry has played a pivotal role in advancing proteomics, enabling the analysis of protein composition in biological samples. Despite the development of various deep learning methods for identifying amino acid sequences (peptides) responsible for observed spectra, challenges persist in \emph{de novo} ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	436,706
2206.05617	Federated Learning with Research Prototypes for Multi-Center MRI-based Detection of Prostate Cancer with Diverse Histopathology	Early prostate cancer detection and staging from MRI are extremely challenging tasks for both radiologists and deep learning algorithms, but the potential to learn from large and diverse datasets remains a promising avenue to increase their generalization capability both within- and across clinics. To enable this for p...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	302,068
2305.08281	FactKB: Generalizable Factuality Evaluation using Language Models Enhanced with Factual Knowledge	Evaluating the factual consistency of automatically generated summaries is essential for the progress and adoption of reliable summarization systems. Despite recent advances, existing factuality evaluation models are not robust, being especially prone to entity and relation errors in new domains. We propose FactKB, a s...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	364,225
2404.07900	High-Dimension Human Value Representation in Large Language Models	The widespread application of Large Language Models (LLMs) across various tasks and fields has necessitated the alignment of these models with human values and preferences. Given various approaches of human value alignment, ranging from Reinforcement Learning with Human Feedback (RLHF), to constitutional learning, etc....	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	446,013
2105.02173	Learning Feature Aggregation for Deep 3D Morphable Models	3D morphable models are widely used for the shape representation of an object class in computer vision and graphics applications. In this work, we focus on deep 3D morphable models that directly apply deep learning on 3D mesh data with a hierarchical structure to capture information at multiple scales. While great effo...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	233,744
2006.06979	Non-Negative Bregman Divergence Minimization for Deep Direct Density Ratio Estimation	Density ratio estimation (DRE) is at the core of various machine learning tasks such as anomaly detection and domain adaptation. In existing studies on DRE, methods based on Bregman divergence (BD) minimization have been extensively studied. However, BD minimization when applied with highly flexible models, such as dee...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	181,634

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