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
2104.00319	Semi-Supervised Domain Adaptation via Selective Pseudo Labeling and Progressive Self-Training	Domain adaptation (DA) is a representation learning methodology that transfers knowledge from a label-sufficient source domain to a label-scarce target domain. While most of early methods are focused on unsupervised DA (UDA), several studies on semi-supervised DA (SSDA) are recently suggested. In SSDA, a small number o...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	227,957
2310.07141	Time and Frequency Offset Estimation and Intercarrier Interference Cancellation for AFDM Systems	Affine frequency division multiplexing (AFDM) is an emerging multicarrier waveform that offers a potential solution for achieving reliable communications over time-varying channels. This paper proposes two maximum-likelihood (ML) estimators of symbol time offset and carrier frequency offset for AFDM systems. One is cal...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	398,839
2308.07491	Adaptive Tracking of a Single-Rigid-Body Character in Various Environments	Since the introduction of DeepMimic [Peng et al. 2018], subsequent research has focused on expanding the repertoire of simulated motions across various scenarios. In this study, we propose an alternative approach for this goal, a deep reinforcement learning method based on the simulation of a single-rigid-body characte...	false	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	true	385,533
2308.15840	MSGNN: Multi-scale Spatio-temporal Graph Neural Network for Epidemic Forecasting	Infectious disease forecasting has been a key focus and proved to be crucial in controlling epidemic. A recent trend is to develop forecast-ing models based on graph neural networks (GNNs). However, existing GNN-based methods suffer from two key limitations: (1) Current models broaden receptive fields by scaling the de...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	388,815
2402.12927	CLIPping the Deception: Adapting Vision-Language Models for Universal Deepfake Detection	The recent advancements in Generative Adversarial Networks (GANs) and the emergence of Diffusion models have significantly streamlined the production of highly realistic and widely accessible synthetic content. As a result, there is a pressing need for effective general purpose detection mechanisms to mitigate the pote...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	431,048
1707.09754	Delay Analysis of Multichannel Parallel Contention Tree Algorithms (MP-CTA)	Contention tree algorithm is initially invented as a solution to improve the stable throughput problem of Slotted ALOHA in multiple access schemes. Even though the throughput is stabilized in tree algorithms, the delay of requests may grow to infinity with respect to the arrival rate of the system. Delay depends heavil...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	78,068
2412.12839	From An LLM Swarm To A PDDL-Empowered HIVE: Planning Self-Executed Instructions In A Multi-Modal Jungle	In response to the call for agent-based solutions that leverage the ever-increasing capabilities of the deep models' ecosystem, we introduce Hive -- a comprehensive solution for selecting appropriate models and subsequently planning a set of atomic actions to satisfy the end-users' instructions. Hive operates over sets...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	518,052
1407.0749	Projecting Ising Model Parameters for Fast Mixing	Inference in general Ising models is difficult, due to high treewidth making tree-based algorithms intractable. Moreover, when interactions are strong, Gibbs sampling may take exponential time to converge to the stationary distribution. We present an algorithm to project Ising model parameters onto a parameter set that...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	34,360
2401.08930	3D Human Pose Analysis via Diffusion Synthesis	Diffusion models have demonstrated remarkable success in generative modeling. In this paper, we propose PADS (Pose Analysis by Diffusion Synthesis), a novel framework designed to address various challenges in 3D human pose analysis through a unified pipeline. Central to PADS are two distinctive strategies: i) learning ...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	422,079
2404.11987	MultiPhys: Multi-Person Physics-aware 3D Motion Estimation	We introduce MultiPhys, a method designed for recovering multi-person motion from monocular videos. Our focus lies in capturing coherent spatial placement between pairs of individuals across varying degrees of engagement. MultiPhys, being physically aware, exhibits robustness to jittering and occlusions, and effectivel...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	447,691
1902.11163	On Maintaining Linear Convergence of Distributed Learning and Optimization under Limited Communication	In distributed optimization and machine learning, multiple nodes coordinate to solve large problems. To do this, the nodes need to compress important algorithm information to bits so that it can be communicated over a digital channel. The communication time of these algorithms follows a complex interplay between a) the...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	122,885
2308.13503	Attending Generalizability in Course of Deep Fake Detection by Exploring Multi-task Learning	This work explores various ways of exploring multi-task learning (MTL) techniques aimed at classifying videos as original or manipulated in cross-manipulation scenario to attend generalizability in deep fake scenario. The dataset used in our evaluation is FaceForensics++, which features 1000 original videos manipulated...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	387,945
2412.04847	MTSpark: Enabling Multi-Task Learning with Spiking Neural Networks for Generalist Agents	Currently, state-of-the-art RL methods excel in single-task settings, but they still struggle to generalize across multiple tasks due to catastrophic forgetting challenges, where previously learned tasks are forgotten as new tasks are introduced. This multi-task learning capability is significantly important for genera...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	true	false	false	514,595
2407.19430	Progressive Domain Adaptation for Thermal Infrared Object Tracking	Due to the lack of large-scale labeled Thermal InfraRed (TIR) training datasets, most existing TIR trackers are trained directly on RGB datasets. However, tracking methods trained on RGB datasets suffer a significant drop-off in TIR data due to the domain shift issue. To this end, in this work, we propose a Progressive...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	476,783
2202.05239	F8Net: Fixed-Point 8-bit Only Multiplication for Network Quantization	Neural network quantization is a promising compression technique to reduce memory footprint and save energy consumption, potentially leading to real-time inference. However, there is a performance gap between quantized and full-precision models. To reduce it, existing quantization approaches require high-precision INT3...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	true	false	true	279,811
2407.18467	Machine Unlearning using a Multi-GAN based Model	This article presents a new machine unlearning approach that utilizes multiple Generative Adversarial Network (GAN) based models. The proposed method comprises two phases: i) data reorganization in which synthetic data using the GAN model is introduced with inverted class labels of the forget datasets, and ii) fine-tun...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	476,380
2501.02504	Watch Video, Catch Keyword: Context-aware Keyword Attention for Moment Retrieval and Highlight Detection	The goal of video moment retrieval and highlight detection is to identify specific segments and highlights based on a given text query. With the rapid growth of video content and the overlap between these tasks, recent works have addressed both simultaneously. However, they still struggle to fully capture the overall v...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	522,513
cs/0601044	Genetic Programming, Validation Sets, and Parsimony Pressure	Fitness functions based on test cases are very common in Genetic Programming (GP). This process can be assimilated to a learning task, with the inference of models from a limited number of samples. This paper is an investigation on two methods to improve generalization in GP-based learning: 1) the selection of the best...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	539,186
2102.06740	Appearance of Random Matrix Theory in Deep Learning	We investigate the local spectral statistics of the loss surface Hessians of artificial neural networks, where we discover excellent agreement with Gaussian Orthogonal Ensemble statistics across several network architectures and datasets. These results shed new light on the applicability of Random Matrix Theory to mode...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	219,852
2209.07999	Self-Supervised Learning with an Information Maximization Criterion	Self-supervised learning allows AI systems to learn effective representations from large amounts of data using tasks that do not require costly labeling. Mode collapse, i.e., the model producing identical representations for all inputs, is a central problem to many self-supervised learning approaches, making self-super...	false	false	false	false	true	false	true	false	false	true	false	true	false	false	false	false	false	false	317,969
0801.0938	Cognitive Networks Achieve Throughput Scaling of a Homogeneous Network	We study two distinct, but overlapping, networks that operate at the same time, space, and frequency. The first network consists of $n$ randomly distributed \emph{primary users}, which form either an ad hoc network, or an infrastructure-supported ad hoc network with $l$ additional base stations. The second network cons...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	1,129
1909.01138	LoopX: Visualizing and understanding the origins of dynamic model behavior	It is a fundamental precept of System Dynamics that structure leads to behavior. Clearly relating the two is one of the roadblocks in the widespread use of feedback models as it normally depends on substantial experimentation or the application of specialized analytic techniques that are not easily approachable by most...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	143,824
2207.12377	A novel Deep Learning approach for one-step Conformal Prediction approximation	Deep Learning predictions with measurable confidence are increasingly desirable for real-world problems, especially in high-risk settings. The Conformal Prediction (CP) framework is a versatile solution that guarantees a maximum error rate given minimal constraints. In this paper, we propose a novel conformal loss func...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	309,992
2005.10635	SymJAX: symbolic CPU/GPU/TPU programming	SymJAX is a symbolic programming version of JAX simplifying graph input/output/updates and providing additional functionalities for general machine learning and deep learning applications. From an user perspective SymJAX provides a la Theano experience with fast graph optimization/compilation and broad hardware support...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	178,242
2203.13612	Repairing Group-Level Errors for DNNs Using Weighted Regularization	Deep Neural Networks (DNNs) have been widely used in software making decisions impacting people's lives. However, they have been found to exhibit severe erroneous behaviors that may lead to unfortunate outcomes. Previous work shows that such misbehaviors often occur due to class property violations rather than errors o...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	true	287,700
1912.11018	Manipulation Planning and Control for Shelf Replenishment	Manipulation planning and control are relevant building blocks of a robotic system and their tight integration is a key factor to improve robot autonomy and allows robots to perform manipulation tasks of increasing complexity, such as those needed in the in-store logistics domain. Supermarkets contain a large variety o...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	158,461
2302.11119	Balanced Line Coverage in Large-scale Urban Scene	Line coverage is to cover linear infrastructure modeled as 1D segments by robots, which received attention in recent years. With the increasing urbanization, the area of the city and the density of infrastructure continues to increase, which brings two issues: (1) Due to the energy constraint, it is hard for the homoge...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	347,096
2104.07749	Actionable Models: Unsupervised Offline Reinforcement Learning of Robotic Skills	We consider the problem of learning useful robotic skills from previously collected offline data without access to manually specified rewards or additional online exploration, a setting that is becoming increasingly important for scaling robot learning by reusing past robotic data. In particular, we propose the objecti...	false	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	230,526
2311.17955	PEAN: A Diffusion-Based Prior-Enhanced Attention Network for Scene Text Image Super-Resolution	Scene text image super-resolution (STISR) aims at simultaneously increasing the resolution and readability of low-resolution scene text images, thus boosting the performance of the downstream recognition task. Two factors in scene text images, visual structure and semantic information, affect the recognition performanc...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	411,493
1303.6001	Generalizing k-means for an arbitrary distance matrix	The original k-means clustering method works only if the exact vectors representing the data points are known. Therefore calculating the distances from the centroids needs vector operations, since the average of abstract data points is undefined. Existing algorithms can be extended for those cases when the sole input i...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	23,234
cmp-lg/9505040	Text Chunking using Transformation-Based Learning	Eric Brill introduced transformation-based learning and showed that it can do part-of-speech tagging with fairly high accuracy. The same method can be applied at a higher level of textual interpretation for locating chunks in the tagged text, including non-recursive ``baseNP'' chunks. For this purpose, it is convenient...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	536,399
2212.04800	AUC Maximization for Low-Resource Named Entity Recognition	Current work in named entity recognition (NER) uses either cross entropy (CE) or conditional random fields (CRF) as the objective/loss functions to optimize the underlying NER model. Both of these traditional objective functions for the NER problem generally produce adequate performance when the data distribution is ba...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	335,578
1612.07297	Finding network communities using modularity density	Many real-world complex networks exhibit a community structure, in which the modules correspond to actual functional units. Identifying these communities is a key challenge for scientists. A common approach is to search for the network partition that maximizes a quality function. Here, we present a detailed analysis of...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	65,924
2112.12083	Predicting treatment effects from observational studies using machine learning methods: A simulation study	Measuring treatment effects in observational studies is challenging because of confounding bias. Confounding occurs when a variable affects both the treatment and the outcome. Traditional methods such as propensity score matching estimate treatment effects by conditioning on the confounders. Recent literature has prese...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	272,877
2303.05279	Can large language models build causal graphs?	Building causal graphs can be a laborious process. To ensure all relevant causal pathways have been captured, researchers often have to discuss with clinicians and experts while also reviewing extensive relevant medical literature. By encoding common and medical knowledge, large language models (LLMs) represent an oppo...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	350,407
2203.04298	CaSS: A Channel-aware Self-supervised Representation Learning Framework for Multivariate Time Series Classification	Self-supervised representation learning of Multivariate Time Series (MTS) is a challenging task and attracts increasing research interests in recent years. Many previous works focus on the pretext task of self-supervised learning and usually neglect the complex problem of MTS encoding, leading to unpromising results. I...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	284,413
2007.15746	Laser2Vec: Similarity-based Retrieval for Robotic Perception Data	As mobile robot capabilities improve and deployment times increase, tools to analyze the growing volume of data are becoming necessary. Current state-of-the-art logging, playback, and exploration systems are insufficient for practitioners seeking to discover systemic points of failure in robotic systems. This paper pre...	false	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	189,746
1805.00628	Understanding Urban Human Mobility through Crowdsensed Data	Understanding how people move in the urban area is important for solving urbanization issues, such as traffic management, urban planning, epidemic control, and communication network improvement. Leveraging recent availability of large amounts of diverse crowdsensed data, many studies have made contributions to this fie...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	96,474
1707.02892	A Generalized Recurrent Neural Architecture for Text Classification with Multi-Task Learning	Multi-task learning leverages potential correlations among related tasks to extract common features and yield performance gains. However, most previous works only consider simple or weak interactions, thereby failing to model complex correlations among three or more tasks. In this paper, we propose a multi-task learnin...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	76,769
2311.15887	FLASC: A Flare-Sensitive Clustering Algorithm	Clustering algorithms are often used to find subpopulations in exploratory data analysis workflows. Not only the clusters themselves, but also their shape can represent meaningful subpopulations. In this paper, we present FLASC, an algorithm that detects branches within clusters to identify such subpopulations. FLASC b...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	true	false	410,673
2409.14603	Brain Surgery: Ensuring GDPR Compliance in Large Language Models via Concept Erasure	As large-scale AI systems proliferate, ensuring compliance with data privacy laws such as the General Data Protection Regulation (GDPR) has become critical. This paper introduces Brain Surgery, a transformative methodology for making every local AI model GDPR-ready by enabling real-time privacy management and targeted ...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	490,546
2412.06263	iLLaVA: An Image is Worth Fewer Than 1/3 Input Tokens in Large Multimodal Models	In this paper, we introduce iLLaVA, a simple method that can be seamlessly deployed upon current Large Vision-Language Models (LVLMs) to greatly increase the throughput with nearly lossless model performance, without a further requirement to train. iLLaVA achieves this by finding and gradually merging the redundant tok...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	515,179
2002.05273	A Second look at Exponential and Cosine Step Sizes: Simplicity, Adaptivity, and Performance	Stochastic Gradient Descent (SGD) is a popular tool in training large-scale machine learning models. Its performance, however, is highly variable, depending crucially on the choice of the step sizes. Accordingly, a variety of strategies for tuning the step sizes have been proposed, ranging from coordinate-wise approach...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	163,843
2501.10483	ArxEval: Evaluating Retrieval and Generation in Language Models for Scientific Literature	Language Models [LMs] are now playing an increasingly large role in information generation and synthesis; the representation of scientific knowledge in these systems needs to be highly accurate. A prime challenge is hallucination; that is, generating apparently plausible but actually false information, including invent...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	525,546
2004.10141	TAEN: Temporal Aware Embedding Network for Few-Shot Action Recognition	Classification of new class entities requires collecting and annotating hundreds or thousands of samples that is often prohibitively costly. Few-shot learning suggests learning to classify new classes using just a few examples. Only a small number of studies address the challenge of few-shot learning on spatio-temporal...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	173,552
1902.00247	Sharp Analysis for Nonconvex SGD Escaping from Saddle Points	In this paper, we give a sharp analysis for Stochastic Gradient Descent (SGD) and prove that SGD is able to efficiently escape from saddle points and find an $(\epsilon, O(\epsilon^{0.5}))$-approximate second-order stationary point in $\tilde{O}(\epsilon^{-3.5})$ stochastic gradient computations for generic nonconvex o...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	120,363
2109.07878	A Medical Pre-Diagnosis System for Histopathological Image of Breast Cancer	This paper constructs a novel intelligent medical diagnosis system, which can realize automatic communication and breast cancer pathological image recognition. This system contains two main parts, including a pre-training chatbot called M-Chatbot and an improved neural network model of EfficientNetV2-S named EfficientN...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	255,689
1806.00381	Persistence paths and signature features in topological data analysis	We introduce a new feature map for barcodes that arise in persistent homology computation. The main idea is to first realize each barcode as a path in a convenient vector space, and to then compute its path signature which takes values in the tensor algebra of that vector space. The composition of these two operations ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	99,297
2308.16095	Food Choice Mimicry on a Large University Campus	Social influence is a strong determinant of food consumption, which in turn influences health. Although consistent observations have been made on the role of social factors in driving similarities in food consumption, much less is known about the precise governing mechanisms. We study social influence on food choice th...	false	false	false	true	false	false	false	false	false	false	false	false	false	true	false	false	false	false	388,898
1406.5582	Optimal Offline Packet Scheduling in Energy Harvesting 2-user Multiple Access Channel with Common Data	The lifetime and the sustainability of the wireless sensor networks (WSNs) can be increased with energy harvesting transmitters utilizing optimum packet scheduling. On the other hand, WSNs are observed to collect spatially or temporally correlated data which should be taken into account for the optimum packet schedulin...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	34,036
2101.08137	Modelling and Optimal Control of Multi Strain Epidemics, with Application to COVID-19	This work introduces a novel epidemiological model that simultaneously considers multiple viral strains, reinfections due to waning immunity response over time and an optimal control formulation. This enables us to derive optimal mitigation strategies over a prescribed time horizon under a more realistic framework that...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	216,238
2410.04568	Ranking Policy Learning via Marketplace Expected Value Estimation From Observational Data	We develop a decision making framework to cast the problem of learning a ranking policy for search or recommendation engines in a two-sided e-commerce marketplace as an expected reward optimization problem using observational data. As a value allocation mechanism, the ranking policy allocates retrieved items to the des...	false	false	false	false	true	true	true	false	false	false	false	false	false	false	false	false	false	false	495,347
1706.02901	Characterizing Types of Convolution in Deep Convolutional Recurrent Neural Networks for Robust Speech Emotion Recognition	Deep convolutional neural networks are being actively investigated in a wide range of speech and audio processing applications including speech recognition, audio event detection and computational paralinguistics, owing to their ability to reduce factors of variations, for learning from speech. However, studies have su...	false	false	true	false	false	false	true	false	true	false	false	false	false	false	false	false	false	true	75,061
2407.11239	From GaLore to WeLore: How Low-Rank Weights Non-uniformly Emerge from Low-Rank Gradients	Modern Large Language Models (LLMs) are composed of matrices with billions of elements, making their storage and processing quite demanding in terms of computational resources and memory usage. Being significantly large, such matrices can often be expressed in low-rank format with potential to relax resource requiremen...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	473,356
2109.14349	Relational Memory: Native In-Memory Accesses on Rows and Columns	Analytical database systems are typically designed to use a column-first data layout to access only the desired fields. On the other hand, storing data row-first works great for accessing, inserting, or updating entire rows. Transforming rows to columns at runtime is expensive, hence, many analytical systems ingest dat...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	true	257,941
2203.00084	Competitors-Aware Stochastic Lap Strategy Optimisation for Race Hybrid Vehicles	World Endurance Championship (WEC) racing events are characterised by a relevant performance gap among competitors. The fastest vehicles category, consisting in hybrid vehicles, has to respect energy usage constraints set by the technical regulation. Considering absence of competitors, i.e. traffic conditions, the opti...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	282,860
2407.16833	Retrieval Augmented Generation or Long-Context LLMs? A Comprehensive Study and Hybrid Approach	Retrieval Augmented Generation (RAG) has been a powerful tool for Large Language Models (LLMs) to efficiently process overly lengthy contexts. However, recent LLMs like Gemini-1.5 and GPT-4 show exceptional capabilities to understand long contexts directly. We conduct a comprehensive comparison between RAG and long-con...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	475,740
1207.7167	Predicate Generation for Learning-Based Quantifier-Free Loop Invariant Inference	We address the predicate generation problem in the context of loop invariant inference. Motivated by the interpolation-based abstraction refinement technique, we apply the interpolation theorem to synthesize predicates implicitly implied by program texts. Our technique is able to improve the effectiveness and efficienc...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	17,832
2401.11271	DACR: Distribution-Augmented Contrastive Reconstruction for Time-Series Anomaly Detection	Anomaly detection in time-series data is crucial for identifying faults, failures, threats, and outliers across a range of applications. Recently, deep learning techniques have been applied to this topic, but they often struggle in real-world scenarios that are complex and highly dynamic, e.g., the normal data may cons...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	422,942
1310.2700	Analyzing Big Data with Dynamic Quantum Clustering	How does one search for a needle in a multi-dimensional haystack without knowing what a needle is and without knowing if there is one in the haystack? This kind of problem requires a paradigm shift - away from hypothesis driven searches of the data - towards a methodology that lets the data speak for itself. Dynamic Qu...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	27,696
2308.03151	Food-500 Cap: A Fine-Grained Food Caption Benchmark for Evaluating Vision-Language Models	Vision-language models (VLMs) have shown impressive performance in substantial downstream multi-modal tasks. However, only comparing the fine-tuned performance on downstream tasks leads to the poor interpretability of VLMs, which is adverse to their future improvement. Several prior works have identified this issue and...	false	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	383,913
2406.16150	Intensity Confusion Matters: An Intensity-Distance Guided Loss for Bronchus Segmentation	Automatic segmentation of the bronchial tree from CT imaging is important, as it provides structural information for disease diagnosis. Despite the merits of previous automatic bronchus segmentation methods, they have paied less attention to the issue we term as \textit{Intensity Confusion}, wherein the intensity value...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	467,017
2412.08541	Euclidean Fast Attention: Machine Learning Global Atomic Representations at Linear Cost	Long-range correlations are essential across numerous machine learning tasks, especially for data embedded in Euclidean space, where the relative positions and orientations of distant components are often critical for accurate predictions. Self-attention offers a compelling mechanism for capturing these global effects,...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	516,132
1612.02542	Minimum Rates of Approximate Sufficient Statistics	Given a sufficient statistic for a parametric family of distributions, one can estimate the parameter without access to the data. However, the memory or code size for storing the sufficient statistic may nonetheless still be prohibitive. Indeed, for $n$ independent samples drawn from a $k$-nomial distribution with $d=k...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	65,244
1412.6157	Epidemic Outbreaks in Networks with Equitable or Almost-Equitable Partitions	We study the diffusion of epidemics on networks that are partitioned into local communities. The gross structure of hierarchical networks of this kind can be described by a quotient graph. The rationale of this approach is that individuals infect those belonging to the same community with higher probability than indivi...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	38,592
2001.11423	Asymptotic regime analysis of NOMA uplink networks under QoS delay Constraints	In the fifth generation and beyond (B5G) technologies, delay constrains emerge as a topic of particular interest for ultra reliable low latency communications (e.g., enhanced reality, haptic communications). In this report, we study the performance of a two user uplink non orthogonal multiple access (NOMA) network unde...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	162,070
2403.04105	Natural Language Processing in Patents: A Survey	Patents, encapsulating crucial technical and legal information, present a rich domain for natural language processing (NLP) applications. As NLP technologies evolve, large language models (LLMs) have demonstrated outstanding capabilities in general text processing and generation tasks. However, the application of LLMs ...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	435,452
2110.02015	Assessment of CFD capability for prediction of the Coand\u{a} effect	The tendency of a jet to stay attached to a flat or convex surface is called the Coand\u{a} effect and has many potential technical applications. The aim of this thesis is to assess how well Computational Fluid Dynamics can capture it. A Reynolds-Averaged Navier-Stokes approach with a 2-dimensional domain was first use...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	258,972
2111.04352	Grassmannian learning mutual subspace method for image set recognition	This paper addresses the problem of object recognition given a set of images as input (e.g., multiple camera sources and video frames). Convolutional neural network (CNN)-based frameworks do not exploit these sets effectively, processing a pattern as observed, not capturing the underlying feature distribution as it doe...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	265,465
2106.03837	MemStream: Memory-Based Streaming Anomaly Detection	Given a stream of entries over time in a multi-dimensional data setting where concept drift is present, how can we detect anomalous activities? Most of the existing unsupervised anomaly detection approaches seek to detect anomalous events in an offline fashion and require a large amount of data for training. This is no...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	239,480
2304.07494	BVIP Guiding System with Adaptability to Individual Differences	Guiding robots can not only detect close-range obstacles like other guiding tools, but also extend its range to perceive the environment when making decisions. However, most existing works over-simplified the interaction between human agents and robots, ignoring the differences between individuals, resulting in poor ex...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	358,367
2407.09017	AI-Driven Guided Response for Security Operation Centers with Microsoft Copilot for Security	Security operation centers contend with a constant stream of security incidents, ranging from straightforward to highly complex. To address this, we developed Microsoft Copilot for Security Guided Response (CGR), an industry-scale ML architecture that guides security analysts across three key tasks -- (1) investigation...	false	false	false	false	false	true	true	false	false	false	false	false	true	false	false	false	false	false	472,414
2105.11618	TR-BERT: Dynamic Token Reduction for Accelerating BERT Inference	Existing pre-trained language models (PLMs) are often computationally expensive in inference, making them impractical in various resource-limited real-world applications. To address this issue, we propose a dynamic token reduction approach to accelerate PLMs' inference, named TR-BERT, which could flexibly adapt the lay...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	236,762
1905.10540	Dynamic Cell Structure via Recursive-Recurrent Neural Networks	In a recurrent setting, conventional approaches to neural architecture search find and fix a general model for all data samples and time steps. We propose a novel algorithm that can dynamically search for the structure of cells in a recurrent neural network model. Based on a combination of recurrent and recursive neura...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	132,095
1906.02005	A surrogate model for computational homogenization of elastostatics at finite strain using the HDMR-based neural network approximator	We propose a surrogate model for two-scale computational homogenization of elastostatics at finite strains. The macroscopic constitutive law is made numerically available via an explicit formulation of the associated macro-energy density. This energy density is constructed by using a neural network architecture that mi...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	133,910
2011.02268	Causal Autoregressive Flows	Two apparently unrelated fields -- normalizing flows and causality -- have recently received considerable attention in the machine learning community. In this work, we highlight an intrinsic correspondence between a simple family of autoregressive normalizing flows and identifiable causal models. We exploit the fact th...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	204,879
2407.12792	Visually Robust Adversarial Imitation Learning from Videos with Contrastive Learning	We propose C-LAIfO, a computationally efficient algorithm designed for imitation learning from videos in the presence of visual mismatch between agent and expert domains. We analyze the problem of imitation from expert videos with visual discrepancies, and introduce a solution for robust latent space estimation using c...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	474,077
2007.13134	Data-efficient visuomotor policy training using reinforcement learning and generative models	We present a data-efficient framework for solving visuomotor sequential decision-making problems which exploits the combination of reinforcement learning (RL) and latent variable generative models. Our framework trains deep visuomotor policies by introducing an action latent variable such that the feed-forward policy s...	false	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	189,032
2406.19549	ASCENT: Amplifying Power Side-Channel Resilience via Learning & Monte-Carlo Tree Search	Power side-channel (PSC) analysis is pivotal for securing cryptographic hardware. Prior art focused on securing gate-level netlists obtained as-is from chip design automation, neglecting all the complexities and potential side-effects for security arising from the design automation process. That is, automation traditio...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	468,458
2411.14773	Mode-conditioned music learning and composition: a spiking neural network inspired by neuroscience and psychology	Musical mode is one of the most critical element that establishes the framework of pitch organization and determines the harmonic relationships. Previous works often use the simplistic and rigid alignment method, and overlook the diversity of modes. However, in contrast to AI models, humans possess cognitive mechanisms...	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	510,318
2106.08086	Decomposition of Global Feature Importance into Direct and Associative Components (DEDACT)	Global model-agnostic feature importance measures either quantify whether features are directly used for a model's predictions (direct importance) or whether they contain prediction-relevant information (associative importance). Direct importance provides causal insight into the model's mechanism, yet it fails to expos...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	241,181
2301.01246	Optimizing Agent Collaboration through Heuristic Multi-Agent Planning	The SOTA algorithms for addressing QDec-POMDP issues, QDec-FP and QDec-FPS, are unable to effectively tackle problems that involve different types of sensing agents. We propose a new algorithm that addresses this issue by requiring agents to adopt the same plan if one agent is unable to take a sensing action but the ot...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	true	false	false	false	339,187
2003.13807	Explicit Regularization of Stochastic Gradient Methods through Duality	We consider stochastic gradient methods under the interpolation regime where a perfect fit can be obtained (minimum loss at each observation). While previous work highlighted the implicit regularization of such algorithms, we consider an explicit regularization framework as a minimum Bregman divergence convex feasibili...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	170,309
2405.10248	Co-Matching: Towards Human-Machine Collaborative Legal Case Matching	Recent efforts have aimed to improve AI machines in legal case matching by integrating legal domain knowledge. However, successful legal case matching requires the tacit knowledge of legal practitioners, which is difficult to verbalize and encode into machines. This emphasizes the crucial role of involving legal practi...	true	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	454,690
2004.12691	Neuromorphic Nearest-Neighbor Search Using Intel's Pohoiki Springs	Neuromorphic computing applies insights from neuroscience to uncover innovations in computing technology. In the brain, billions of interconnected neurons perform rapid computations at extremely low energy levels by leveraging properties that are foreign to conventional computing systems, such as temporal spiking codes...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	174,319
1812.01225	Learning from Extrapolated Corrections	Our goal is to enable robots to learn cost functions from user guidance. Often it is difficult or impossible for users to provide full demonstrations, so corrections have emerged as an easier guidance channel. However, when robots learn cost functions from corrections rather than demonstrations, they have to extrapolat...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	115,475
0812.2785	Prediction of Platinum Prices Using Dynamically Weighted Mixture of Experts	Neural networks are powerful tools for classification and regression in static environments. This paper describes a technique for creating an ensemble of neural networks that adapts dynamically to changing conditions. The model separates the input space into four regions and each network is given a weight in each regio...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	2,801
2107.07970	How Vulnerable Are Automatic Fake News Detection Methods to Adversarial Attacks?	As the spread of false information on the internet has increased dramatically in recent years, more and more attention is being paid to automated fake news detection. Some fake news detection methods are already quite successful. Nevertheless, there are still many vulnerabilities in the detection algorithms. The reason...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	246,581
2311.16675	A Distribution-Based Threshold for Determining Sentence Similarity	We hereby present a solution to a semantic textual similarity (STS) problem in which it is necessary to match two sentences containing, as the only distinguishing factor, highly specific information (such as names, addresses, identification codes), and from which we need to derive a definition for when they are similar...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	411,013
2008.11573	Multi-Label Sentiment Analysis on 100 Languages with Dynamic Weighting for Label Imbalance	We investigate cross-lingual sentiment analysis, which has attracted significant attention due to its applications in various areas including market research, politics and social sciences. In particular, we introduce a sentiment analysis framework in multi-label setting as it obeys Plutchik wheel of emotions. We introd...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	193,313
2409.09741	Benchmarking LLMs in Political Content Text-Annotation: Proof-of-Concept with Toxicity and Incivility Data	This article benchmarked the ability of OpenAI's GPTs and a number of open-source LLMs to perform annotation tasks on political content. We used a novel protest event dataset comprising more than three million digital interactions and created a gold standard that includes ground-truth labels annotated by human coders a...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	488,452
1808.03712	Unsupervised Keyphrase Extraction from Scientific Publications	We propose a novel unsupervised keyphrase extraction approach that filters candidate keywords using outlier detection. It starts by training word embeddings on the target document to capture semantic regularities among the words. It then uses the minimum covariance determinant estimator to model the distribution of non...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	104,980
0904.4774	Dictionary Identification - Sparse Matrix-Factorisation via $\ell_1$-Minimisation	This article treats the problem of learning a dictionary providing sparse representations for a given signal class, via $\ell_1$-minimisation. The problem can also be seen as factorising a $\ddim \times \nsig$ matrix $Y=(y_1 >... y_\nsig), y_n\in \R^\ddim$ of training signals into a $\ddim \times \natoms$ dictionary ma...	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	3,616
2002.08783	Optimal Resource Allocation for Dynamic Product Development Process via Convex Optimization	Resource allocation is an essential aspect of successful Product Development (PD). In this paper, we formulate the dynamic resource allocation of the PD process as a convex optimization problem. Specially, we build and solve two variants of this issue: the budget-constrained problem and the performance-constrained prob...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	164,861
2405.08647	Output-decomposed Learning of Mealy Machines	We present an active automata learning algorithm which learns a decomposition of a finite state machine, based on projecting onto individual outputs. This is dual to a recent compositional learning algorithm by Labbaf et al. (2023). When projecting the outputs to a smaller set, the model itself is reduced in size. By h...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	454,169
2206.08398	Learning Generic Lung Ultrasound Biomarkers for Decoupling Feature Extraction from Downstream Tasks	Contemporary artificial neural networks (ANN) are trained end-to-end, jointly learning both features and classifiers for the task of interest. Though enormously effective, this paradigm imposes significant costs in assembling annotated task-specific datasets and training large-scale networks. We propose to decouple fea...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	303,116
1801.06480	A Practitioners' Guide to Transfer Learning for Text Classification using Convolutional Neural Networks	Transfer Learning (TL) plays a crucial role when a given dataset has insufficient labeled examples to train an accurate model. In such scenarios, the knowledge accumulated within a model pre-trained on a source dataset can be transferred to a target dataset, resulting in the improvement of the target model. Though TL i...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	88,616
2007.07423	Comparing to Learn: Surpassing ImageNet Pretraining on Radiographs By Comparing Image Representations	In deep learning era, pretrained models play an important role in medical image analysis, in which ImageNet pretraining has been widely adopted as the best way. However, it is undeniable that there exists an obvious domain gap between natural images and medical images. To bridge this gap, we propose a new pretraining m...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	187,325
2006.05133	Contestable Black Boxes	The right to contest a decision with consequences on individuals or the society is a well-established democratic right. Despite this right also being explicitly included in GDPR in reference to automated decision-making, its study seems to have received much less attention in the AI literature compared, for example, to...	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	false	false	180,950
1910.01189	Improved Attention Models for Memory Augmented Neural Network Adaptive Controllers	We introduced a {\it working memory} augmented adaptive controller in our recent work. The controller uses attention to read from and write to the working memory. Attention allows the controller to read specific information that is relevant and update its working memory with information based on its relevance. The retr...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	147,862

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