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
2203.14291	Video Polyp Segmentation: A Deep Learning Perspective	We present the first comprehensive video polyp segmentation (VPS) study in the deep learning era. Over the years, developments in VPS are not moving forward with ease due to the lack of large-scale fine-grained segmentation annotations. To address this issue, we first introduce a high-quality frame-by-frame annotated V...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	287,950
1501.04656	Microscopic Advances with Large-Scale Learning: Stochastic Optimization for Cryo-EM	Determining the 3D structures of biological molecules is a key problem for both biology and medicine. Electron Cryomicroscopy (Cryo-EM) is a promising technique for structure estimation which relies heavily on computational methods to reconstruct 3D structures from 2D images. This paper introduces the challenging Cryo-...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	39,391
2209.12592	Generating Compressed Combinatory Proof Structures -- An Approach to Automated First-Order Theorem Proving	Representing a proof tree by a combinator term that reduces to the tree lets subtle forms of duplication within the tree materialize as duplicated subterms of the combinator term. In a DAG representation of the combinator term these straightforwardly factor into shared subgraphs. To search for proofs, combinator terms ...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	319,584
1703.08198	On Desirable Semantics of Functional Dependencies over Databases with Incomplete Information	Codd's relational model describes just one possible world. To better cope with incomplete information, extended database models allow several possible worlds. Vague tables are one such convenient extended model where attributes accept sets of possible values (e.g., the manager is either Jill or Bob). However, conceptua...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	70,536
2407.06785	Towards physics-informed neural networks for landslide prediction	For decades, solutions to regional scale landslide prediction have mostly relied on data-driven models, by definition, disconnected from the physics of the failure mechanism. The success and spread of such tools came from the ability to exploit proxy variables rather than explicit geotechnical ones, as the latter are p...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	471,534
2205.11027	When Data Geometry Meets Deep Function: Generalizing Offline Reinforcement Learning	In offline reinforcement learning (RL), one detrimental issue to policy learning is the error accumulation of deep Q function in out-of-distribution (OOD) areas. Unfortunately, existing offline RL methods are often over-conservative, inevitably hurting generalization performance outside data distribution. In our study,...	false	false	false	false	true	false	true	true	false	false	false	false	false	false	false	false	false	false	297,966
2407.05858	Fast On-device LLM Inference with NPUs	On-device inference for Large Language Models (LLMs), driven by increasing privacy concerns and advancements of mobile-sized models, has gained significant interest. However, even mobile-sized LLMs (e.g., Gemma-2B) encounter unacceptably high inference latency, often bottlenecked by the prefill stage in tasks like scre...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	471,155
2502.02433	A coding theoretic study of homogeneous Markovian predictive games	This paper explores a predictive game in which a Forecaster announces odds based on a time-homogeneous Markov kernel, establishing a game-theoretic law of large numbers for the relative frequencies of occurrences of all finite strings. A key feature of our proof is a betting strategy built on a universal coding scheme,...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	530,311
2109.14638	Privacy Policy Question Answering Assistant: A Query-Guided Extractive Summarization Approach	Existing work on making privacy policies accessible has explored new presentation forms such as color-coding based on the risk factors or summarization to assist users with conscious agreement. To facilitate a more personalized interaction with the policies, in this work, we propose an automated privacy policy question...	false	false	false	false	false	true	false	false	true	false	false	false	false	true	false	false	false	false	258,011
2406.04608	A Recover-then-Discriminate Framework for Robust Anomaly Detection	Anomaly detection (AD) has been extensively studied and applied in a wide range of scenarios in the recent past. However, there are still gaps between achieved and desirable levels of recognition accuracy for making AD for practical applications. In this paper, we start from an insightful analysis of two types of funda...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	461,755
1001.4122	Distributed Control of the Laplacian Spectral Moments of a Network	It is well-known that the eigenvalue spectrum of the Laplacian matrix of a network contains valuable information about the network structure and the behavior of many dynamical processes run on it. In this paper, we propose a fully decentralized algorithm that iteratively modifies the structure of a network of agents in...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	5,496
2303.05892	Object-Aware Distillation Pyramid for Open-Vocabulary Object Detection	Open-vocabulary object detection aims to provide object detectors trained on a fixed set of object categories with the generalizability to detect objects described by arbitrary text queries. Previous methods adopt knowledge distillation to extract knowledge from Pretrained Vision-and-Language Models (PVLMs) and transfe...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	350,624
2309.05254	Towards Better Data Exploitation in Self-Supervised Monocular Depth Estimation	Depth estimation plays an important role in the robotic perception system. Self-supervised monocular paradigm has gained significant attention since it can free training from the reliance on depth annotations. Despite recent advancements, existing self-supervised methods still underutilize the available training data, ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	391,015
2401.02606	Exploiting Polarized Material Cues for Robust Car Detection	Car detection is an important task that serves as a crucial prerequisite for many automated driving functions. The large variations in lighting/weather conditions and vehicle densities of the scenes pose significant challenges to existing car detection algorithms to meet the highly accurate perception demand for safety...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	419,775
2101.10495	Transparency in Multi-Human Multi-Robot Interaction	Transparency is a key factor in improving the performance of human-robot interaction. A transparent interface allows humans to be aware of the state of a robot and to assess the progress of the tasks at hand. When multi-robot systems are involved, transparency is an even greater challenge, due to the larger number of v...	true	false	false	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	216,954
2011.12845	Bounds for Algorithmic Mutual Information and a Unifilar Order Estimator	Inspired by Hilberg's hypothesis, which states that mutual information between blocks for natural language grows like a power law, we seek for links between power-law growth rate of algorithmic mutual information and of some estimator of the unifilar order, i.e., the number of hidden states in the generating stationary...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	208,291
2008.02796	Learning to Factorize and Relight a City	We propose a learning-based framework for disentangling outdoor scenes into temporally-varying illumination and permanent scene factors. Inspired by the classic intrinsic image decomposition, our learning signal builds upon two insights: 1) combining the disentangled factors should reconstruct the original image, and 2...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	190,716
1304.1494	Now that I Have a Good Theory of Uncertainty, What Else Do I Need?	Rather than discussing the isolated merits of a nominative theory of uncertainty, this paper focuses on a class of problems, referred to as Dynamic Classification Problem (DCP), which requires the integration of many theories, including a prescriptive theory of uncertainty. We start by analyzing the Dynamic Classificat...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	23,527
2205.11798	Symbolic Expression Transformer: A Computer Vision Approach for Symbolic Regression	Symbolic Regression (SR) is a type of regression analysis to automatically find the mathematical expression that best fits the data. Currently, SR still basically relies on various searching strategies so that a sample-specific model is required to be optimized for every expression, which significantly limits the model...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	298,288
2412.04326	Understanding Student Sentiment on Mental Health Support in Colleges Using Large Language Models	Mental health support in colleges is vital in educating students by offering counseling services and organizing supportive events. However, evaluating its effectiveness faces challenges like data collection difficulties and lack of standardized metrics, limiting research scope. Student feedback is crucial for evaluatio...	false	false	false	false	true	false	false	false	true	false	false	false	false	true	false	false	false	false	514,363
2303.15322	Progressive Semantic-Visual Mutual Adaption for Generalized Zero-Shot Learning	Generalized Zero-Shot Learning (GZSL) identifies unseen categories by knowledge transferred from the seen domain, relying on the intrinsic interactions between visual and semantic information. Prior works mainly localize regions corresponding to the sharing attributes. When various visual appearances correspond to the ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	354,450
2005.07647	Finding Experts in Transformer Models	In this work we study the presence of expert units in pre-trained Transformer Models (TM), and how they impact a model's performance. We define expert units to be neurons that are able to classify a concept with a given average precision, where a concept is represented by a binary set of sentences containing the concep...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	177,335
1811.07725	Cyclic bent functions and their applications in codes, codebooks, designs, MUBs and sequences	Let $m$ be an even positive integer. A Boolean bent function $f$ on $\GF{m-1} \times \GF {}$ is called a \emph{cyclic bent function} if for any $a\neq b\in \GF {m-1}$ and $\epsilon \in \GF{}$, $f(ax_1,x_2)+f(bx_1,x_2+\epsilon)$ is always bent, where $x_1\in \GF {m-1}, x_2 \in \GF {}$. Cyclic bent functions look extreme...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	113,851
2412.08100	FuzzDistill: Intelligent Fuzzing Target Selection using Compile-Time Analysis and Machine Learning	Fuzz testing is a fundamental technique employed to identify vulnerabilities within software systems. However, the process can be protracted and resource-intensive, especially when confronted with extensive codebases. In this work, I present FuzzDistill, an approach that harnesses compile-time data and machine learning...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	true	515,938
1509.04110	Cooperative Cognitive Radio Network with Energy Harvesting: Stability Analysis	This paper investigates the maximum stable throughput of a cooperative cognitive radio system with energy harvesting Primary User and Secondary User. Each PU and SU has a data queue for data storage and a battery for energy storage. These batteries harvest energy from the environment and store it for data transmission ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	46,895
2311.03313	Practical considerations for variable screening in the Super Learner	Estimating a prediction function is a fundamental component of many data analyses. The Super Learner ensemble, a particular implementation of stacking, has desirable theoretical properties and has been used successfully in many applications. Dimension reduction can be accomplished by using variable screening algorithms...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	405,795
1906.04663	Control contribution identifies top driver nodes in complex networks	We propose a new measure to quantify the impact of a node $i$ in controlling a directed network. This measure, called `control contribution' $\mathcal{C}_{i}$, combines the probability for node $i$ to appear in a set of driver nodes and the probability for other nodes to be controlled by $i$. To calculate $\mathcal{C}_...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	134,792
2005.02659	Towards Building Knowledge by Merging Multiple Ontologies with CoMerger: A Partitioning-based Approach	Ontologies are the prime way of organizing data in the Semantic Web. Often, it is necessary to combine several, independently developed ontologies to obtain a knowledge graph fully representing a domain of interest. The complementarity of existing ontologies can be leveraged by merging them. Existing approaches for ont...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	175,945
2207.11720	Progressive Feature Learning for Realistic Cloth-Changing Gait Recognition	Gait recognition is instrumental in crime prevention and social security, for it can be conducted at a long distance to figure out the identity of persons. However, existing datasets and methods cannot satisfactorily deal with the most challenging cloth-changing problem in practice. Specifically, the practical gait mod...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	309,753
2211.16106	Encoder-Decoder Model for Suffix Prediction in Predictive Monitoring	Predictive monitoring is a subfield of process mining that aims to predict how a running case will unfold in the future. One of its main challenges is forecasting the sequence of activities that will occur from a given point in time -- suffix prediction -- . Most approaches to the suffix prediction problem learn to pre...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	333,519
2406.17747	Probing the effects of broken symmetries in machine learning	Symmetry is one of the most central concepts in physics, and it is no surprise that it has also been widely adopted as an inductive bias for machine-learning models applied to the physical sciences. This is especially true for models targeting the properties of matter at the atomic scale. Both established and state-of-...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	467,704
2011.01223	Comprehensible Counterfactual Explanation on Kolmogorov-Smirnov Test	The Kolmogorov-Smirnov (KS) test is popularly used in many applications, such as anomaly detection, astronomy, database security and AI systems. One challenge remained untouched is how we can obtain an explanation on why a test set fails the KS test. In this paper, we tackle the problem of producing counterfactual expl...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	204,528
2410.19814	Stochastic Flow Matching for Resolving Small-Scale Physics	Conditioning diffusion and flow models have proven effective for super-resolving small-scale details in natural images.However, in physical sciences such as weather, super-resolving small-scale details poses significant challenges due to: (i) misalignment between input and output distributions (i.e., solutions to disti...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	502,502
1906.00904	Deep ReLU Networks Have Surprisingly Few Activation Patterns	The success of deep networks has been attributed in part to their expressivity: per parameter, deep networks can approximate a richer class of functions than shallow networks. In ReLU networks, the number of activation patterns is one measure of expressivity; and the maximum number of patterns grows exponentially with ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	133,539
2103.06357	ReportAGE: Automatically extracting the exact age of Twitter users based on self-reports in tweets	Advancing the utility of social media data for research applications requires methods for automatically detecting demographic information about social media study populations, including users' age. The objective of this study was to develop and evaluate a method that automatically identifies the exact age of users base...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	224,264
2405.20045	Iterative Learning Control of Fast, Nonlinear, Oscillatory Dynamics (Preprint)	The sudden onset of deleterious and oscillatory dynamics (often called instabilities) is a known challenge in many fluid, plasma, and aerospace systems. These dynamics are difficult to address because they are nonlinear, chaotic, and are often too fast for active control schemes. In this work, we develop an alternative...	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	459,171
2310.00627	Intelligent Client Selection for Federated Learning using Cellular Automata	Federated Learning (FL) has emerged as a promising solution for privacy-enhancement and latency minimization in various real-world applications, such as transportation, communications, and healthcare. FL endeavors to bring Machine Learning (ML) down to the edge by harnessing data from million of devices and IoT sensors...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	true	396,067
2311.04925	Investigating Deep-Learning NLP for Automating the Extraction of Oncology Efficacy Endpoints from Scientific Literature	Benchmarking drug efficacy is a critical step in clinical trial design and planning. The challenge is that much of the data on efficacy endpoints is stored in scientific papers in free text form, so extraction of such data is currently a largely manual task. Our objective is to automate this task as much as possible. I...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	406,402
2401.05069	MISS: Multiclass Interpretable Scoring Systems	In this work, we present a novel, machine-learning approach for constructing Multiclass Interpretable Scoring Systems (MISS) - a fully data-driven methodology for generating single, sparse, and user-friendly scoring systems for multiclass classification problems. Scoring systems are commonly utilized as decision suppor...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	420,637
2005.05961	Private Two-Terminal Hypothesis Testing	We study private two-terminal hypothesis testing with simple hypotheses where the privacy goal is to ensure that participating in the testing protocol reveals little additional information about the other user's observation when a user is told what the correct hypothesis is. We show that, in general, meaningful correct...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	176,881
2104.10103	Space Partitioning and Regression Mode Seeking via a Mean-Shift-Inspired Algorithm	The mean shift (MS) algorithm is a nonparametric method used to cluster sample points and find the local modes of kernel density estimates, using an idea based on iterative gradient ascent. In this paper we develop a mean-shift-inspired algorithm to estimate the modes of regression functions and partition the sample po...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	231,465
1712.00716	Convolutional Phase Retrieval via Gradient Descent	We study the convolutional phase retrieval problem, of recovering an unknown signal $\mathbf x \in \mathbb C^n $ from $m$ measurements consisting of the magnitude of its cyclic convolution with a given kernel $\mathbf a \in \mathbb C^m $. This model is motivated by applications such as channel estimation, optics, and u...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	85,969
2012.09276	Measuring Disentanglement: A Review of Metrics	Learning to disentangle and represent factors of variation in data is an important problem in AI. While many advances have been made to learn these representations, it is still unclear how to quantify disentanglement. While several metrics exist, little is known on their implicit assumptions, what they truly measure, a...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	212,008
2207.10436	Mining Relations among Cross-Frame Affinities for Video Semantic Segmentation	The essence of video semantic segmentation (VSS) is how to leverage temporal information for prediction. Previous efforts are mainly devoted to developing new techniques to calculate the cross-frame affinities such as optical flow and attention. Instead, this paper contributes from a different angle by mining relations...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	309,266
2001.05060	Recognizing Video Events with Varying Rhythms	Recognizing Video events in long, complex videos with multiple sub-activities has received persistent attention recently. This task is more challenging than traditional action recognition with short, relatively homogeneous video clips. In this paper, we investigate the problem of recognizing long and complex events wit...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	160,428
2405.18523	MM-Mixing: Multi-Modal Mixing Alignment for 3D Understanding	We introduce MM-Mixing, a multi-modal mixing alignment framework for 3D understanding. MM-Mixing applies mixing-based methods to multi-modal data, preserving and optimizing cross-modal connections while enhancing diversity and improving alignment across modalities. Our proposed two-stage training pipeline combines feat...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	458,457
1410.2862	On the Oblivious Transfer Capacity of Generalized Erasure Channels against Malicious Adversaries	Noisy channels are a powerful resource for cryptography as they can be used to obtain information-theoretically secure key agreement, commitment and oblivious transfer protocols, among others. Oblivious transfer (OT) is a fundamental primitive since it is complete for secure multi-party computation, and the OT capacity...	false	false	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	36,656
2101.09606	Learning degraded image classification with restoration data fidelity	Learning-based methods especially with convolutional neural networks (CNN) are continuously showing superior performance in computer vision applications, ranging from image classification to restoration. For image classification, most existing works focus on very clean images such as images in Caltech-256 and ImageNet ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	216,652
1911.12615	Data Transmission based on Exact Inverse Periodic Nonlinear Fourier Transform, Part II: Waveform Design and Experiment	The nonlinear Fourier transform has the potential to overcome limits on performance and achievable data rates which arise in modern optical fiber communication systems when nonlinear interference is treated as noise. The periodic nonlinear Fourier transform (PNFT) has been much less investigated compared to its counter...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	155,453
2309.08580	Automated Characterization and Monitoring of Material Shape using Riemannian Geometry	Shape affects both the physical and chemical properties of a material. Characterizing the roughness, convexity, and general geometry of a material can yield information on its catalytic efficiency, solubility, elasticity, porosity, and overall effectiveness in the application of interest. However, material shape can be...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	392,228
1808.00171	Shuffle-Then-Assemble: Learning Object-Agnostic Visual Relationship Features	Due to the fact that it is prohibitively expensive to completely annotate visual relationships, i.e., the (obj1, rel, obj2) triplets, relationship models are inevitably biased to object classes of limited pairwise patterns, leading to poor generalization to rare or unseen object combinations. Therefore, we are interest...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	104,322
0809.1618	ECOLANG - Communications Language for Ecological Simulations Network	This document describes the communication language used in one multiagent system environment for ecological simulations, based on EcoDynamo simulator application linked with several intelligent agents and visualisation applications, and extends the initial definition of the language. The agents actions and perceptions ...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	true	false	false	false	2,322
1901.08763	Continuous Analog Channel Estimation Aided Beamforming for Massive MIMO Systems	Analog beamforming greatly reduces the implementation cost of massive antenna transceivers by using only one up/down-conversion chain. However, it incurs a large pilot overhead when used with conventional channel estimation (CE) techniques. This is because these CE techniques involve digital processing, requiring the u...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	119,575
1906.04563	Latent Channel Networks	Latent Euclidean embedding models a given network by representing each node in a Euclidean space, where the probability of two nodes sharing an edge is a function of the distances between the nodes. This implies that for two nodes to share an edge with high probability, they must be relatively close in all dimensions. ...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	134,755
2305.00723	Predictions Based on Pixel Data: Insights from PDEs and Finite Differences	As supported by abundant experimental evidence, neural networks are state-of-the-art for many approximation tasks in high-dimensional spaces. Still, there is a lack of a rigorous theoretical understanding of what they can approximate, at which cost, and at which accuracy. One network architecture of practical use, espe...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	361,438
1909.08929	Automobile Theft Detection by Clustering Owner Driver Data	As automobiles become intelligent, automobile theft methods are evolving intelligently. Therefore automobile theft detection has become a major research challenge. Data-mining, biometrics, and additional authentication methods have been proposed to address automobile theft, in previous studies. Among these methods, dat...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	146,096
2306.01890	Mixed-type Distance Shrinkage and Selection for Clustering via Kernel Metric Learning	Distance-based clustering and classification are widely used in various fields to group mixed numeric and categorical data. In many algorithms, a predefined distance measurement is used to cluster data points based on their dissimilarity. While there exist numerous distance-based measures for data with pure numerical a...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	370,655
2405.04534	Tactile-Augmented Radiance Fields	We present a scene representation, which we call a tactile-augmented radiance field (TaRF), that brings vision and touch into a shared 3D space. This representation can be used to estimate the visual and tactile signals for a given 3D position within a scene. We capture a scene's TaRF from a collection of photos and sp...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	452,596
2204.08339	Migrating Face Swap to Mobile Devices: A lightweight Framework and A Supervised Training Solution	Existing face swap methods rely heavily on large-scale networks for adequate capacity to generate visually plausible results, which inhibits its applications on resource-constraint platforms. In this work, we propose MobileFSGAN, a novel lightweight GAN for face swap that can run on mobile devices with much fewer param...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	292,050
2201.12354	Discovering Nonlinear PDEs from Scarce Data with Physics-encoded Learning	There have been growing interests in leveraging experimental measurements to discover the underlying partial differential equations (PDEs) that govern complex physical phenomena. Although past research attempts have achieved great success in data-driven PDE discovery, the robustness of the existing methods cannot be gu...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	277,613
2006.12433	What shapes feature representations? Exploring datasets, architectures, and training	In naturalistic learning problems, a model's input contains a wide range of features, some useful for the task at hand, and others not. Of the useful features, which ones does the model use? Of the task-irrelevant features, which ones does the model represent? Answers to these questions are important for understanding ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	183,588
1307.2991	Integrity Verification for Outsourcing Uncertain Frequent Itemset Mining	In recent years, due to the wide applications of uncertain data (e.g., noisy data), uncertain frequent itemsets (UFI) mining over uncertain databases has attracted much attention, which differs from the corresponding deterministic problem from the generalized definition and resolutions. As the most costly task in assoc...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	25,759
2007.00493	Optimisation of the PointPillars network for 3D object detection in point clouds	In this paper we present our research on the optimisation of a deep neural network for 3D object detection in a point cloud. Techniques like quantisation and pruning available in the Brevitas and PyTorch tools were used. We performed the experiments for the PointPillars network, which offers a reasonable compromise bet...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	185,134
2209.09393	Mitigating Representation Bias in Action Recognition: Algorithms and Benchmarks	Deep learning models have achieved excellent recognition results on large-scale video benchmarks. However, they perform poorly when applied to videos with rare scenes or objects, primarily due to the bias of existing video datasets. We tackle this problem from two different angles: algorithm and dataset. From the persp...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	318,485
2101.00203	B-SMALL: A Bayesian Neural Network approach to Sparse Model-Agnostic Meta-Learning	There is a growing interest in the learning-to-learn paradigm, also known as meta-learning, where models infer on new tasks using a few training examples. Recently, meta-learning based methods have been widely used in few-shot classification, regression, reinforcement learning, and domain adaptation. The model-agnostic...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	214,006
2305.19879	RaSP: Relation-aware Semantic Prior for Weakly Supervised Incremental Segmentation	Class-incremental semantic image segmentation assumes multiple model updates, each enriching the model to segment new categories. This is typically carried out by providing expensive pixel-level annotations to the training algorithm for all new objects, limiting the adoption of such methods in practical applications. A...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	369,720
2404.11310	Autonomous aerial perching and unperching using omnidirectional tiltrotor and switching controller	Aerial unperching of multirotors has received little attention as opposed to perching that has been investigated to elongate operation time. This study presents a new aerial robot capable of both perching and unperching autonomously on/from a ferromagnetic surface during flight, and a switching controller to avoid roto...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	447,456
1903.01182	Complement Objective Training	Learning with a primary objective, such as softmax cross entropy for classification and sequence generation, has been the norm for training deep neural networks for years. Although being a widely-adopted approach, using cross entropy as the primary objective exploits mostly the information from the ground-truth class f...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	123,206
2204.12717	Dataset for Robust and Accurate Leading Vehicle Velocity Recognition	Recognition of the surrounding environment using a camera is an important technology in Advanced Driver-Assistance Systems and Autonomous Driving, and recognition technology is often solved by machine learning approaches such as deep learning in recent years. Machine learning requires datasets for learning and evaluati...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	293,581
2206.10256	Human-in-the-loop Speaker Adaptation for DNN-based Multi-speaker TTS	This paper proposes a human-in-the-loop speaker-adaptation method for multi-speaker text-to-speech. With a conventional speaker-adaptation method, a target speaker's embedding vector is extracted from his/her reference speech using a speaker encoder trained on a speaker-discriminative task. However, this method cannot ...	true	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	303,849
2105.11455	Managing HILP Consequences Using Dynamic Distribution System Asset Assessment	In order to increase the resilience of distribution systems against high-impact low-probability (HILP) events, it is important to prioritize assets damaged by these events so that the lost loads, especially sensitive and important loads, can be recovered faster. For this reason, this paper discusses the prioritization ...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	236,708
1907.07854	Understanding Video Content: Efficient Hero Detection and Recognition for the Game "Honor of Kings"	In order to understand content and automatically extract labels for videos of the game "Honor of Kings", it is necessary to detect and recognize characters (called "hero") together with their camps in the game video. In this paper, we propose an efficient two-stage algorithm to detect and recognize heros in game videos...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	138,985
2404.01255	Gradient Methods for Scalable Multi-value Electricity Network Expansion Planning	We consider multi-value expansion planning (MEP), a general bilevel optimization model in which a planner optimizes arbitrary functions of the dispatch outcome in the presence of a partially controllable, competitive electricity market. The MEP problem can be used to jointly plan various grid assets, such as transmissi...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	443,339
2307.02276	First-Explore, then Exploit: Meta-Learning to Solve Hard Exploration-Exploitation Trade-Offs	Standard reinforcement learning (RL) agents never intelligently explore like a human (i.e. taking into account complex domain priors and adapting quickly based on previous exploration). Across episodes, RL agents struggle to perform even simple exploration strategies, for example systematic search that avoids exploring...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	377,645
1911.05020	Generative adversarial networks (GAN) based efficient sampling of chemical space for inverse design of inorganic materials	A major challenge in materials design is how to efficiently search the vast chemical design space to find the materials with desired properties. One effective strategy is to develop sampling algorithms that can exploit both explicit chemical knowledge and implicit composition rules embodied in the large materials datab...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	153,145
2501.05749	Bridging Dialects: Translating Standard Bangla to Regional Variants Using Neural Models	The Bangla language includes many regional dialects, adding to its cultural richness. The translation of Bangla Language into regional dialects presents a challenge due to significant variations in vocabulary, pronunciation, and sentence structure across regions like Chittagong, Sylhet, Barishal, Noakhali, and Mymensin...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	523,708
1410.2045	Supervised learning Methods for Bangla Web Document Categorization	This paper explores the use of machine learning approaches, or more specifically, four supervised learning Methods, namely Decision Tree(C 4.5), K-Nearest Neighbour (KNN), Na\"ive Bays (NB), and Support Vector Machine (SVM) for categorization of Bangla web documents. This is a task of automatically sorting a set of doc...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	36,585
1608.00218	Hyperparameter Transfer Learning through Surrogate Alignment for Efficient Deep Neural Network Training	Recently, several optimization methods have been successfully applied to the hyperparameter optimization of deep neural networks (DNNs). The methods work by modeling the joint distribution of hyperparameter values and corresponding error. Those methods become less practical when applied to modern DNNs whose training ma...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	true	false	false	59,245
2311.14823	Revisiting Quantum Algorithms for Linear Regressions: Quadratic Speedups without Data-Dependent Parameters	Linear regression is one of the most fundamental linear algebra problems. Given a dense matrix $A \in \mathbb{R}^{n \times d}$ and a vector $b$, the goal is to find $x'$ such that $ \\| Ax' - b \\|_2^2 \leq (1+\epsilon) \min_{x} \\| A x - b \\|_2^2 $. The best classical algorithm takes $O(nd) + \mathrm{poly}(d/\epsilon)$...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	410,267
2306.15731	Stochastic Gradient Bayesian Optimal Experimental Designs for Simulation-based Inference	Simulation-based inference (SBI) methods tackle complex scientific models with challenging inverse problems. However, SBI models often face a significant hurdle due to their non-differentiable nature, which hampers the use of gradient-based optimization techniques. Bayesian Optimal Experimental Design (BOED) is a power...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	376,127
2103.14580	Correcting Automated and Manual Speech Transcription Errors using Warped Language Models	Masked language models have revolutionized natural language processing systems in the past few years. A recently introduced generalization of masked language models called warped language models are trained to be more robust to the types of errors that appear in automatic or manual transcriptions of spoken language by ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	226,901
1912.00953	LOGAN: Latent Optimisation for Generative Adversarial Networks	Training generative adversarial networks requires balancing of delicate adversarial dynamics. Even with careful tuning, training may diverge or end up in a bad equilibrium with dropped modes. In this work, we improve CS-GAN with natural gradient-based latent optimisation and show that it improves adversarial dynamics b...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	155,940
2410.17532	Responsible Multilingual Large Language Models: A Survey of Development, Applications, and Societal Impact	Multilingual Large Language Models (MLLMs) represent a pivotal advancement in democratizing artificial intelligence across linguistic boundaries. While theoretical foundations are well-established, practical implementation guidelines remain scattered. This work bridges this gap by providing a comprehensive end-to-end f...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	501,508
2208.03431	IVT: An End-to-End Instance-guided Video Transformer for 3D Pose Estimation	Video 3D human pose estimation aims to localize the 3D coordinates of human joints from videos. Recent transformer-based approaches focus on capturing the spatiotemporal information from sequential 2D poses, which cannot model the contextual depth feature effectively since the visual depth features are lost in the step...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	311,781
1209.2139	Fused Multiple Graphical Lasso	In this paper, we consider the problem of estimating multiple graphical models simultaneously using the fused lasso penalty, which encourages adjacent graphs to share similar structures. A motivating example is the analysis of brain networks of Alzheimer's disease using neuroimaging data. Specifically, we may wish to e...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	18,489
2106.02943	Learning Routines for Effective Off-Policy Reinforcement Learning	The performance of reinforcement learning depends upon designing an appropriate action space, where the effect of each action is measurable, yet, granular enough to permit flexible behavior. So far, this process involved non-trivial user choices in terms of the available actions and their execution frequency. We propos...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	239,098
1403.5199	Obtaining Information about Queries behind Views and Dependencies	We consider the problems of finding and determining certain query answers and of determining containment between queries; each problem is formulated in presence of materialized views and dependencies under the closed-world assumption. We show a tight relationship between the problems in this setting. Further, we introd...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	true	31,708
1806.00974	ALMN: Deep Embedding Learning with Geometrical Virtual Point Generating	Deep embedding learning becomes more attractive for discriminative feature learning, but many methods still require hard-class mining, which is computationally complex and performance-sensitive. To this end, we propose Adaptive Large Margin N-Pair loss (ALMN) to address the aforementioned issues. Instead of exploring h...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	99,454
2405.15903	UnitNorm: Rethinking Normalization for Transformers in Time Series	Normalization techniques are crucial for enhancing Transformer models' performance and stability in time series analysis tasks, yet traditional methods like batch and layer normalization often lead to issues such as token shift, attention shift, and sparse attention. We propose UnitNorm, a novel approach that scales in...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	457,153
1802.02379	Dynamic Sampling from a Discrete Probability Distribution with a Known Distribution of Rates	In this paper, we consider several efficient data structures for the problem of sampling from a dynamically changing discrete probability distribution, where some prior information is known on the distribution of the rates, in particular the maximum and minimum rate, and where the number of possible outcomes N is large...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	89,764
2105.11233	Gradient descent in materia through homodyne gradient extraction	Deep learning, a multi-layered neural network approach inspired by the brain, has revolutionized machine learning. One of its key enablers has been backpropagation, an algorithm that computes the gradient of a loss function with respect to the weights and biases in the neural network model, in combination with its use ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	true	236,634
1810.01406	Super-Resolution via Conditional Implicit Maximum Likelihood Estimation	Single-image super-resolution (SISR) is a canonical problem with diverse applications. Leading methods like SRGAN produce images that contain various artifacts, such as high-frequency noise, hallucinated colours and shape distortions, which adversely affect the realism of the result. In this paper, we propose an altern...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	true	false	true	109,390
2212.03232	Learning the joint distribution of two sequences using little or no paired data	We present a noisy channel generative model of two sequences, for example text and speech, which enables uncovering the association between the two modalities when limited paired data is available. To address the intractability of the exact model under a realistic data setup, we propose a variational inference approxim...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	335,038
1806.05947	Discovering User Groups for Natural Language Generation	We present a model which predicts how individual users of a dialog system understand and produce utterances based on user groups. In contrast to previous work, these user groups are not specified beforehand, but learned in training. We evaluate on two referring expression (RE) generation tasks; our experiments show tha...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	100,590
2206.08477	Backdoor Attacks on Vision Transformers	Vision Transformers (ViT) have recently demonstrated exemplary performance on a variety of vision tasks and are being used as an alternative to CNNs. Their design is based on a self-attention mechanism that processes images as a sequence of patches, which is quite different compared to CNNs. Hence it is interesting to ...	false	false	false	false	false	false	true	false	false	false	false	true	true	false	false	false	false	false	303,155
2008.12680	Bayesian Neural Networks for Uncertainty Estimation of Imaging Biomarkers	Image segmentation enables to extract quantitative measures from scans that can serve as imaging biomarkers for diseases. However, segmentation quality can vary substantially across scans, and therefore yield unfaithful estimates in the follow-up statistical analysis of biomarkers. The core problem is that segmentation...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	193,648
2412.09483	Early Detection of At-Risk Students Using Machine Learning	This research presents preliminary work to address the challenge of identifying at-risk students using supervised machine learning and three unique data categories: engagement, demographics, and performance data collected from Fall 2023 using Canvas and the California State University, Fullerton dashboard. We aim to ta...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	516,502
2202.04185	OSM-tree: A Sortedness-Aware Index	Indexes facilitate efficient querying when the selection predicate is on an indexed key. As a result, when loading data, if we anticipate future selective (point or range) queries, we typically maintain an index that is gradually populated as new data is ingested. In that respect, indexing can be perceived as the proce...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	true	279,480
2404.17528	Geometry-aware Reconstruction and Fusion-refined Rendering for Generalizable Neural Radiance Fields	Generalizable NeRF aims to synthesize novel views for unseen scenes. Common practices involve constructing variance-based cost volumes for geometry reconstruction and encoding 3D descriptors for decoding novel views. However, existing methods show limited generalization ability in challenging conditions due to inaccura...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	449,890
2302.05094	General, Single-shot, Target-less, and Automatic LiDAR-Camera Extrinsic Calibration Toolbox	This paper presents an open source LiDAR-camera calibration toolbox that is general to LiDAR and camera projection models, requires only one pairing of LiDAR and camera data without a calibration target, and is fully automatic. For automatic initial guess estimation, we employ the SuperGlue image matching pipeline to f...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	344,930

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