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
1409.2578	Feedback Control of Switched Stochastic Systems Using Randomly Available Active Mode Information	Almost sure asymptotic stabilization of a discrete-time switched stochastic system is investigated. Information on the active operation mode of the switched system is assumed to be available for control purposes only at random time instants. We propose a stabilizing feedback control framework that utilizes the informat...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	35,919
0901.2396	Joint Source-Channel Coding at the Application Layer for Parallel Gaussian Sources	In this paper the multicasting of independent parallel Gaussian sources over a binary erasure broadcasted channel is considered. Multiresolution embedded quantizer and layered joint source-channel coding schemes are used in order to serve simultaneously several users at different channel capacities. The convex nature o...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	2,988
2011.06819	diagNNose: A Library for Neural Activation Analysis	In this paper we introduce diagNNose, an open source library for analysing the activations of deep neural networks. diagNNose contains a wide array of interpretability techniques that provide fundamental insights into the inner workings of neural networks. We demonstrate the functionality of diagNNose with a case study...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	206,353
1405.1472	An Exploration of the Role of Principal Inertia Components in Information Theory	The principal inertia components of the joint distribution of two random variables $X$ and $Y$ are inherently connected to how an observation of $Y$ is statistically related to a hidden variable $X$. In this paper, we explore this connection within an information theoretic framework. We show that, under certain symmetr...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	32,882
1103.1559	Minimum Pseudoweight Analysis of 3-Dimensional Turbo Codes	In this work, we consider pseudocodewords of (relaxed) linear programming (LP) decoding of 3-dimensional turbo codes (3D-TCs). We present a relaxed LP decoder for 3D-TCs, adapting the relaxed LP decoder for conventional turbo codes proposed by Feldman in his thesis. We show that the 3D-TC polytope is proper and $C$-sym...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	9,529
2311.10736	Systematic Evaluation of Applying Space-Filling Curves to Automotive Maneuver Detection	Identifying driving maneuvers plays an essential role on-board vehicles to monitor driving and driver states, as well as off-board to train and evaluate machine learning algorithms for automated driving for example. Maneuvers can be characterized by vehicle kinematics or data from its surroundings including other traff...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	408,625
1602.00904	Comparative evaluation of state-of-the-art algorithms for SSVEP-based BCIs	Brain-computer interfaces (BCIs) have been gaining momentum in making human-computer interaction more natural, especially for people with neuro-muscular disabilities. Among the existing solutions the systems relying on electroencephalograms (EEG) occupy the most prominent place due to their non-invasiveness. However, t...	true	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	51,632
2306.10728	AdaSelection: Accelerating Deep Learning Training through Data Subsampling	In this paper, we introduce AdaSelection, an adaptive sub-sampling method to identify the most informative sub-samples within each minibatch to speed up the training of large-scale deep learning models without sacrificing model performance. Our method is able to flexibly combines an arbitrary number of baseline sub-sam...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	374,340
1611.01957	Linear Convergence of SVRG in Statistical Estimation	SVRG and its variants are among the state of art optimization algorithms for large scale machine learning problems. It is well known that SVRG converges linearly when the objective function is strongly convex. However this setup can be restrictive, and does not include several important formulations such as Lasso, grou...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	63,474
2302.04562	NLP-based Decision Support System for Examination of Eligibility Criteria from Securities Prospectuses at the German Central Bank	As part of its digitization initiative, the German Central Bank (Deutsche Bundesbank) wants to examine the extent to which natural Language Processing (NLP) can be used to make independent decisions upon the eligibility criteria of securities prospectuses. Every month, the Directorate General Markets at the German Cent...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	344,750
2309.16055	Identifying Risk Factors for Post-COVID-19 Mental Health Disorders: A Machine Learning Perspective	In this study, we leveraged machine learning techniques to identify risk factors associated with post-COVID-19 mental health disorders. Our analysis, based on data collected from 669 patients across various provinces in Iraq, yielded valuable insights. We found that age, gender, and geographical region of residence wer...	false	false	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	395,198
2408.16442	Integrating Features for Recognizing Human Activities through Optimized Parameters in Graph Convolutional Networks and Transformer Architectures	Human activity recognition is a major field of study that employs computer vision, machine vision, and deep learning techniques to categorize human actions. The field of deep learning has made significant progress, with architectures that are extremely effective at capturing human dynamics. This study emphasizes the in...	false	false	false	false	true	false	false	true	false	false	false	true	false	false	false	false	false	false	484,331
2304.08492	STRAP: Structured Object Affordance Segmentation with Point Supervision	With significant annotation savings, point supervision has been proven effective for numerous 2D and 3D scene understanding problems. This success is primarily attributed to the structured output space; i.e., samples with high spatial affinity tend to share the same labels. Sharing this spirit, we study affordance segm...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	358,734
2012.08216	FMODetect: Robust Detection of Fast Moving Objects	We propose the first learning-based approach for fast moving objects detection. Such objects are highly blurred and move over large distances within one video frame. Fast moving objects are associated with a deblurring and matting problem, also called deblatting. We show that the separation of deblatting into consecuti...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	211,706
2006.01561	Studying The Effect of MIL Pooling Filters on MIL Tasks	There are different multiple instance learning (MIL) pooling filters used in MIL models. In this paper, we study the effect of different MIL pooling filters on the performance of MIL models in real world MIL tasks. We designed a neural network based MIL framework with 5 different MIL pooling filters: `max', `mean', `at...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	179,809
1711.10394	Exposing Computer Generated Images by Using Deep Convolutional Neural Networks	The recent computer graphics developments have upraised the quality of the generated digital content, astonishing the most skeptical viewer. Games and movies have taken advantage of this fact but, at the same time, these advances have brought serious negative impacts like the ones yielded by fakeimages produced with ma...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	85,591
2110.12064	Causal Effect Identification with Context-specific Independence Relations of Control Variables	We study the problem of causal effect identification from observational distribution given the causal graph and some context-specific independence (CSI) relations. It was recently shown that this problem is NP-hard, and while a sound algorithm to learn the causal effects is proposed in Tikka et al. (2019), no complete ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	262,696
2403.10099	KP-RED: Exploiting Semantic Keypoints for Joint 3D Shape Retrieval and Deformation	In this paper, we present KP-RED, a unified KeyPoint-driven REtrieval and Deformation framework that takes object scans as input and jointly retrieves and deforms the most geometrically similar CAD models from a pre-processed database to tightly match the target. Unlike existing dense matching based methods that typica...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	438,054
2206.00501	Benign Overfitting in Classification: Provably Counter Label Noise with Larger Models	Studies on benign overfitting provide insights for the success of overparameterized deep learning models. In this work, we examine whether overfitting is truly benign in real-world classification tasks. We start with the observation that a ResNet model overfits benignly on Cifar10 but not benignly on ImageNet. To under...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	300,146
2207.07331	Modeling Multi-interest News Sequence for News Recommendation	A session-based news recommender system recommends the next news to a user by modeling the potential interests embedded in a sequence of news read/clicked by her/him in a session. Generally, a user's interests are diverse, namely there are multiple interests corresponding to different types of news, e.g., news of disti...	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	false	308,176
2502.00529	Graph Data Management and Graph Machine Learning: Synergies and Opportunities	The ubiquity of machine learning, particularly deep learning, applied to graphs is evident in applications ranging from cheminformatics (drug discovery) and bioinformatics (protein interaction prediction) to knowledge graph-based query answering, fraud detection, and social network analysis. Concurrently, graph data ma...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	529,422
2301.13088	Stationary Kernels and Gaussian Processes on Lie Groups and their Homogeneous Spaces II: non-compact symmetric spaces	Gaussian processes are arguably the most important class of spatiotemporal models within machine learning. They encode prior information about the modeled function and can be used for exact or approximate Bayesian learning. In many applications, particularly in physical sciences and engineering, but also in areas such ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	342,775
1205.2628	Multiple Source Adaptation and the Renyi Divergence	This paper presents a novel theoretical study of the general problem of multiple source adaptation using the notion of Renyi divergence. Our results build on our previous work [12], but significantly broaden the scope of that work in several directions. We extend previous multiple source loss guarantees based on distri...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	15,935
2008.13191	Caching Transient Content for IoT Sensing: Multi-Agent Soft Actor-Critic	Edge nodes (ENs) in Internet of Things commonly serve as gateways to cache sensing data while providing accessing services for data consumers. This paper considers multiple ENs that cache sensing data under the coordination of the cloud. Particularly, each EN can fetch content generated by sensors within its coverage, ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	193,786
1903.08228	How to Make Swarms Open-Ended? Evolving Collective Intelligence Through a Constricted Exploration of Adjacent Possibles	We propose an approach of open-ended evolution via the simulation of swarm dynamics. In nature, swarms possess remarkable properties, which allow many organisms, from swarming bacteria to ants and flocking birds, to form higher-order structures that enhance their behavior as a group. Swarm simulations highlight three i...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	true	false	true	124,790
2005.04153	A Hybrid Method for Training Convolutional Neural Networks	Artificial Intelligence algorithms have been steadily increasing in popularity and usage. Deep Learning, allows neural networks to be trained using huge datasets and also removes the need for human extracted features, as it automates the feature learning process. In the hearth of training deep neural networks, such as ...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	true	false	false	176,371
2112.09093	Network Realization Functions for Optimal Distributed Control	In this paper, we discuss a distributed control architecture, aimed at networks with linear and time-invariant dynamics, which is amenable to convex formulations for controller design. The proposed approach is well suited for large scale systems, since the resulting feedback schemes completely avoid the exchange of int...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	272,032
1808.00736	Dynamic Adaptation on Non-Stationary Visual Domains	Domain adaptation aims to learn models on a supervised source domain that perform well on an unsupervised target. Prior work has examined domain adaptation in the context of stationary domain shifts, i.e. static data sets. However, with large-scale or dynamic data sources, data from a defined domain is not usually avai...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	104,448
0707.1534	An Architecture Framework for Complex Data Warehouses	Nowadays, many decision support applications need to exploit data that are not only numerical or symbolic, but also multimedia, multistructure, multisource, multimodal, and/or multiversion. We term such data complex data. Managing and analyzing complex data involves a lot of different issues regarding their structure, ...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	415
2306.02348	Leverage Points in Modality Shifts: Comparing Language-only and Multimodal Word Representations	Multimodal embeddings aim to enrich the semantic information in neural representations of language compared to text-only models. While different embeddings exhibit different applicability and performance on downstream tasks, little is known about the systematic representation differences attributed to the visual modali...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	370,866
1610.06920	Bit-pragmatic Deep Neural Network Computing	We quantify a source of ineffectual computations when processing the multiplications of the convolutional layers in Deep Neural Networks (DNNs) and propose Pragmatic (PRA), an architecture that exploits it improving performance and energy efficiency. The source of these ineffectual computations is best understood in th...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	true	62,714
2201.07402	Flexible Parallel Learning in Edge Scenarios: Communication, Computational and Energy Cost	Traditionally, distributed machine learning takes the guise of (i) different nodes training the same model (as in federated learning), or (ii) one model being split among multiple nodes (as in distributed stochastic gradient descent). In this work, we highlight how fog- and IoT-based scenarios often require combining b...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	276,016
2005.00402	Workgroup Mapping: Visual Analysis of Collaboration Culture	The digital transformation of work presents new opportunities to understand how informal workgroups organize around the dynamic needs of organizations, potentially in contrast to the formal, static, and idealized hierarchies depicted by org charts. We present a design study that spans multiple enabling capabilities for...	true	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	175,225
1907.09585	Cooperative Pollution Source Localization and Cleanup with a Bio-inspired Swarm Robot Aggregation	Using robots for exploration of extreme and hazardous environments has the potential to significantly improve human safety. For example, robotic solutions can be deployed to find the source of a chemical leakage and clean the contaminated area. This paper demonstrates a proof-of-concept bio-inspired exploration method ...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	139,402
2211.04023	A Dynamic Graph Interactive Framework with Label-Semantic Injection for Spoken Language Understanding	Multi-intent detection and slot filling joint models are gaining increasing traction since they are closer to complicated real-world scenarios. However, existing approaches (1) focus on identifying implicit correlations between utterances and one-hot encoded labels in both tasks while ignoring explicit label characteri...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	329,112
2212.13407	Hybrid Message Passing Algorithm for Downlink FDD Massive MIMO-OFDM Channel Estimation	The design of message passing (MP) algorithms on factor graphs is an effective manner to implement channel estimation (CE) in wireless communication systems, which performance can be further improved by exploiting prior probability models that accurately match the channel characteristics. In this work, we study the CE ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	338,295
2106.02543	Accelerating Dynamical System Simulations with Contracting and Physics-Projected Neural-Newton Solvers	Recent advances in deep learning have allowed neural networks (NNs) to successfully replace traditional numerical solvers in many applications, thus enabling impressive computing gains. One such application is time domain simulation, which is indispensable for the design, analysis and operation of many engineering syst...	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	238,913
2310.01720	Perceiver-based CDF Modeling for Time Series Forecasting	Transformers have demonstrated remarkable efficacy in forecasting time series data. However, their extensive dependence on self-attention mechanisms demands significant computational resources, thereby limiting their practical applicability across diverse tasks, especially in multimodal problems. In this work, we propo...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	396,539
1910.02600	Deep Evidential Regression	Deterministic neural networks (NNs) are increasingly being deployed in safety critical domains, where calibrated, robust, and efficient measures of uncertainty are crucial. In this paper, we propose a novel method for training non-Bayesian NNs to estimate a continuous target as well as its associated evidence in order ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	148,293
1009.2631	Google matrix of business process management	Development of efficient business process models and determination of their characteristic properties are subject of intense interdisciplinary research. Here, we consider a business process model as a directed graph. Its nodes correspond to the units identified by the modeler and the link direction indicates the causal...	false	false	false	false	false	true	false	false	false	false	false	false	false	true	false	false	false	false	7,543
2305.10358	NUANCE: Near Ultrasound Attack On Networked Communication Environments	This study investigates a primary inaudible attack vector on Amazon Alexa voice services using near ultrasound trojans and focuses on characterizing the attack surface and examining the practical implications of issuing inaudible voice commands. The research maps each attack vector to a tactic or technique from the MIT...	false	false	true	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	365,020
1107.0922	GraphLab: A Distributed Framework for Machine Learning in the Cloud	Machine Learning (ML) techniques are indispensable in a wide range of fields. Unfortunately, the exponential increase of dataset sizes are rapidly extending the runtime of sequential algorithms and threatening to slow future progress in ML. With the promise of affordable large-scale parallel computing, Cloud systems of...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	11,160
2306.01375	Robust and Generalisable Segmentation of Subtle Epilepsy-causing Lesions: a Graph Convolutional Approach	Focal cortical dysplasia (FCD) is a leading cause of drug-resistant focal epilepsy, which can be cured by surgery. These lesions are extremely subtle and often missed even by expert neuroradiologists. "Ground truth" manual lesion masks are therefore expensive, limited and have large inter-rater variability. Existing FC...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	370,425
2501.12299	Sublinear Variational Optimization of Gaussian Mixture Models with Millions to Billions of Parameters	Gaussian Mixture Models (GMMs) range among the most frequently used machine learning models. However, training large, general GMMs becomes computationally prohibitive for datasets with many data points $N$ of high-dimensionality $D$. For GMMs with arbitrary covariances, we here derive a highly efficient variational app...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	526,251
1903.08329	On Sampling Random Features From Empirical Leverage Scores: Implementation and Theoretical Guarantees	Random features provide a practical framework for large-scale kernel approximation and supervised learning. It has been shown that data-dependent sampling of random features using leverage scores can significantly reduce the number of features required to achieve optimal learning bounds. Leverage scores introduce an op...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	124,809
2111.11213	Learn Quasi-stationary Distributions of Finite State Markov Chain	We propose a reinforcement learning (RL) approach to compute the expression of quasi-stationary distribution. Based on the fixed-point formulation of quasi-stationary distribution, we minimize the KL-divergence of two Markovian path distributions induced by the candidate distribution and the true target distribution. T...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	267,590
2311.15924	Diagnosis driven Anomaly Detection for CPS	In Cyber-Physical Systems (CPS) research, anomaly detection (detecting abnormal behavior) and diagnosis (identifying the underlying root cause) are often treated as distinct, isolated tasks. However, diagnosis algorithms require symptoms, i.e. temporally and spatially isolated anomalies, as input. Thus, anomaly detecti...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	410,685
2111.15123	Outage and Finite-SNR DMT Analysis for IRS-aided MIMO Systems: How Large IRSs Need to Be?	Intelligent reflecting surfaces (IRSs) are promising enablers for high-capacity wireless communication systems by constructing favorable channels between the transmitter and receiver. However, general, accurate, and tractable outage analysis for IRS-aided multiple-input-multiple-output (MIMO) systems is not available i...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	268,818
1705.10899	Propositional Knowledge Representation and Reasoning in Restricted Boltzmann Machines	While knowledge representation and reasoning are considered the keys for human-level artificial intelligence, connectionist networks have been shown successful in a broad range of applications due to their capacity for robust learning and flexible inference under uncertainty. The idea of representing symbolic knowledge...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	74,491
2312.08749	Mitigating Label Bias in Machine Learning: Fairness through Confident Learning	Discrimination can occur when the underlying unbiased labels are overwritten by an agent with potential bias, resulting in biased datasets that unfairly harm specific groups and cause classifiers to inherit these biases. In this paper, we demonstrate that despite only having access to the biased labels, it is possible ...	false	false	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	415,435
1206.6735	Elimination of Spurious Ambiguity in Transition-Based Dependency Parsing	We present a novel technique to remove spurious ambiguity from transition systems for dependency parsing. Our technique chooses a canonical sequence of transition operations (computation) for a given dependency tree. Our technique can be applied to a large class of bottom-up transition systems, including for instance N...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	17,038
2107.06638	Procedural Content Generation using Behavior Trees (PCGBT)	Behavior trees (BTs) are a popular method for modeling NPC and enemy AI behavior and have been widely used in commercial games. In this work, rather than use BTs to model game playing agents, we use them for modeling game design agents, defining behaviors as content generation tasks rather than in-game actions. Similar...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	246,150
2211.04903	Novel Chapter Abstractive Summarization using Spinal Tree Aware Sub-Sentential Content Selection	Summarizing novel chapters is a difficult task due to the input length and the fact that sentences that appear in the desired summaries draw content from multiple places throughout the chapter. We present a pipelined extractive-abstractive approach where the extractive step filters the content that is passed to the abs...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	329,382
2107.11503	Efficient Inverse Design of 2D Elastic Metamaterial Systems Using Invertible Neural Networks	Locally resonant elastic metamaterials (LREM) can be designed, by optimizing the geometry of the constituent self-repeating unit cells, to potentially damp out vibration in selected frequency ranges, thus yielding desired bandgaps. However, it remains challenging to quickly arrive at unit cell designs that satisfy any ...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	247,605
1510.07104	Supporting Window Analytics over Large-scale Dynamic Graphs	In relational DBMS, window functions have been widely used to facilitate data analytics. Surprisingly, while similar concepts have been employed for graph analytics, there has been no explicit notions of graph window analytic functions. In this paper, we formally introduce window queries for graph analytics. In such qu...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	48,167
2005.06624	Comparative Analysis of Text Classification Approaches in Electronic Health Records	Text classification tasks which aim at harvesting and/or organizing information from electronic health records are pivotal to support clinical and translational research. However these present specific challenges compared to other classification tasks, notably due to the particular nature of the medical lexicon and lan...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	177,059
2410.14815	Adapting Multilingual LLMs to Low-Resource Languages using Continued Pre-training and Synthetic Corpus	Multilingual LLMs support a variety of languages; however, their performance is suboptimal for low-resource languages. In this work, we emphasize the importance of continued pre-training of multilingual LLMs and the use of translation-based synthetic pre-training corpora for improving LLMs in low-resource languages. We...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	500,223
2112.08106	Enhance Connectivity of Promising Regions for Sampling-based Path Planning	Sampling-based path planning algorithms usually implement uniform sampling methods to search the state space. However, uniform sampling may lead to unnecessary exploration in many scenarios, such as the environment with a few dead ends. Our previous work proposes to use the promising region to guide the sampling proces...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	271,698
2004.00428	Stability and Instability Divergence Conditions for Dynamical Systems	A novel method for stability and instability study of autonomous dynamical systems using the flow and divergence of the vector field is proposed. A relation between the method of Lyapunov functions and the proposed method is established. Bendixon and Bendixon-Dulac theorems for $n$th dimensional systems are extended. B...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	170,632
2303.10353	Sharpness-Aware Gradient Matching for Domain Generalization	The goal of domain generalization (DG) is to enhance the generalization capability of the model learned from a source domain to other unseen domains. The recently developed Sharpness-Aware Minimization (SAM) method aims to achieve this goal by minimizing the sharpness measure of the loss landscape. Though SAM and its v...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	352,410
2112.03551	Optimal Scheduling of Energy Storage for Power System with Capability of Sensing Short-term Future PV Power Production	Constant rise in energy consumption that comes with the population growth and introduction of new technologies has posed critical issues such as efficient energy management on the consumer side. That has elevated the importance of the use of renewable energy sources, particularly photovoltaic (PV) system and wind turbi...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	270,248
1505.04887	A Near-optimal User Ordering Algorithm for Non-iterative Interference Alignment Transceiver Design in MIMO Interfering Broadcast Channels	Interference alignment (IA) has recently emerged as a promising interference mitigation technique for interference networks. In this letter, we focus on the IA non-iterative transceiver design problem in a multiple-input-multiple-output interfering broadcast channel (MIMO-IBC), and observed that there is previously une...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	43,237
cs/0602088	Towards Low-Complexity Linear-Programming Decoding	We consider linear-programming (LP) decoding of low-density parity-check (LDPC) codes. While it is clear that one can use any general-purpose LP solver to solve the LP that appears in the decoding problem, we argue in this paper that the LP at hand is equipped with a lot of structure that one should take advantage of. ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	539,296
2309.01377	Memory augment is All You Need for image restoration	Image restoration is a low-level vision task, most CNN methods are designed as a black box, lacking transparency and internal aesthetics. Although some methods combining traditional optimization algorithms with DNNs have been proposed, they all have some limitations. In this paper, we propose a three-granularity memory...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	389,665
2403.12892	Uoc luong kenh truyen trong he thong da robot su dung SDR	This study focuses on developing an experimental system for estimating communication channels in a multi-robot mobile system using software-defined radio (SDR) devices. The system consists of two mobile robots programmed for two scenarios: one where the robot remains stationary and another where it follows a predefined...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	439,378
2412.01306	Multimodal Medical Disease Classification with LLaMA II	Medical patient data is always multimodal. Images, text, age, gender, histopathological data are only few examples for different modalities in this context. Processing and integrating this multimodal data with deep learning based methods is of utmost interest due to its huge potential for medical procedure such as diag...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	513,062
2306.11044	Frequency effects in Linear Discriminative Learning	Word frequency is a strong predictor in most lexical processing tasks. Thus, any model of word recognition needs to account for how word frequency effects arise. The Discriminative Lexicon Model (DLM; Baayen et al., 2018a, 2019) models lexical processing with linear mappings between words' forms and their meanings. So ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	374,458
2304.10321	DropDim: A Regularization Method for Transformer Networks	We introduceDropDim, a structured dropout method designed for regularizing the self-attention mechanism, which is a key component of the transformer. In contrast to the general dropout method, which randomly drops neurons, DropDim drops part of the embedding dimensions. In this way, the semantic information can be comp...	false	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	359,373
2411.11530	SeqProFT: Applying LoRA Finetuning for Sequence-only Protein Property Predictions	Protein language models (PLMs) are capable of learning the relationships between protein sequences and functions by treating amino acid sequences as textual data in a self-supervised manner. However, fine-tuning these models typically demands substantial computational resources and time, with results that may not alway...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	509,090
2402.14270	Take the Bull by the Horns: Hard Sample-Reweighted Continual Training Improves LLM Generalization	In the rapidly advancing arena of large language models (LLMs), a key challenge is to enhance their capabilities amid a looming shortage of high-quality training data. Our study starts from an empirical strategy for the light continual training of LLMs using their original pre-training data sets, with a specific focus ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	431,600
2206.07077	Comparison of Different Configurations of Saturated Core Fault Current Limiters in a Power Grid by Numerical Method	Short circuit fault currents are increasing due to growing demand for electricity and high complexity in power systems. Because the fault currents reach the highest value which the breakers are unable to restrict, the electrical grid security is under jeopardy. By entering a limiting impedance into a transmission line ...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	302,587
2202.01802	Different Affordances on Facebook and SMS Text Messaging Do Not Impede Generalization of Language-Based Predictive Models	Adaptive mobile device-based health interventions often use machine learning models trained on non-mobile device data, such as social media text, due to the difficulty and high expense of collecting large text message (SMS) data. Therefore, understanding the differences and generalization of models between these platfo...	true	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	278,588
2303.08577	Investigating GANsformer: A Replication Study of a State-of-the-Art Image Generation Model	The field of image generation through generative modelling is abundantly discussed nowadays. It can be used for various applications, such as up-scaling existing images, creating non-existing objects, such as interior design scenes, products or even human faces, and achieving transfer-learning processes. In this contex...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	351,696
2409.13774	Trustworthy Intrusion Detection: Confidence Estimation Using Latent Space	This work introduces a novel method for enhancing confidence in anomaly detection in Intrusion Detection Systems (IDS) through the use of a Variational Autoencoder (VAE) architecture. By developing a confidence metric derived from latent space representations, we aim to improve the reliability of IDS predictions agains...	false	false	false	false	true	false	true	false	false	false	false	false	true	false	false	false	false	false	490,167
2310.07915	Tag Your Fish in the Broken Net: A Responsible Web Framework for Protecting Online Privacy and Copyright	The World Wide Web, a ubiquitous source of information, serves as a primary resource for countless individuals, amassing a vast amount of data from global internet users. However, this online data, when scraped, indexed, and utilized for activities like web crawling, search engine indexing, and, notably, AI model train...	false	false	false	true	false	false	false	false	false	false	false	false	false	true	false	false	false	true	399,178
1607.07939	A Sensorimotor Reinforcement Learning Framework for Physical Human-Robot Interaction	Modeling of physical human-robot collaborations is generally a challenging problem due to the unpredictive nature of human behavior. To address this issue, we present a data-efficient reinforcement learning framework which enables a robot to learn how to collaborate with a human partner. The robot learns the task from ...	false	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	59,090
2412.16199	Stabilizing Machine Learning for Reproducible and Explainable Results: A Novel Validation Approach to Subject-Specific Insights	Machine Learning is transforming medical research by improving diagnostic accuracy and personalizing treatments. General ML models trained on large datasets identify broad patterns across populations, but their effectiveness is often limited by the diversity of human biology. This has led to interest in subject-specifi...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	519,405
1909.07745	Adversarial Feature Training for Generalizable Robotic Visuomotor Control	Deep reinforcement learning (RL) has enabled training action-selection policies, end-to-end, by learning a function which maps image pixels to action outputs. However, it's application to visuomotor robotic policy training has been limited because of the challenge of large-scale data collection when working with physic...	false	false	false	false	false	false	true	true	false	false	false	true	false	false	false	false	false	false	145,765
2210.05033	Multilingual Representation Distillation with Contrastive Learning	Multilingual sentence representations from large models encode semantic information from two or more languages and can be used for different cross-lingual information retrieval and matching tasks. In this paper, we integrate contrastive learning into multilingual representation distillation and use it for quality estim...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	322,670
2309.04668	Influence Maximization in Social Networks: A Survey	Online social networks have become an important platform for people to communicate, share knowledge and disseminate information. Given the widespread usage of social media, individuals' ideas, preferences and behavior are often influenced by their peers or friends in the social networks that they participate in. Since ...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	390,802
2212.08619	Planting and Mitigating Memorized Content in Predictive-Text Language Models	Language models are widely deployed to provide automatic text completion services in user products. However, recent research has revealed that language models (especially large ones) bear considerable risk of memorizing private training data, which is then vulnerable to leakage and extraction by adversaries. In this st...	false	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	336,811
2110.09962	PR-CIM: a Variation-Aware Binary-Neural-Network Framework for Process-Resilient Computation-in-memory	Binary neural networks (BNNs) that use 1-bit weights and activations have garnered interest as extreme quantization provides low power dissipation. By implementing BNNs as computing-in-memory (CIM), which computes multiplication and accumulations on memory arrays in an analog fashion, namely analog CIM, we can further ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	261,989
1111.5720	A GP-MOEA/D Approach for Modelling Total Electron Content over Cyprus	Vertical Total Electron Content (vTEC) is an ionospheric characteristic used to derive the signal delay imposed by the ionosphere on near-vertical trans-ionospheric links. The major aim of this paper is to design a prediction model based on the main factors that influence the variability of this parameter on a diurnal,...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	false	false	13,162
2310.07929	Crosslingual Structural Priming and the Pre-Training Dynamics of Bilingual Language Models	Do multilingual language models share abstract grammatical representations across languages, and if so, when do these develop? Following Sinclair et al. (2022), we use structural priming to test for abstract grammatical representations with causal effects on model outputs. We extend the approach to a Dutch-English bili...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	399,185
2005.06223	DREAM Architecture: a Developmental Approach to Open-Ended Learning in Robotics	Robots are still limited to controlled conditions, that the robot designer knows with enough details to endow the robot with the appropriate models or behaviors. Learning algorithms add some flexibility with the ability to discover the appropriate behavior given either some demonstrations or a reward to guide its explo...	false	false	false	false	true	false	true	true	false	false	false	false	false	false	false	true	false	false	176,950
2403.13829	DecompOpt: Controllable and Decomposed Diffusion Models for Structure-based Molecular Optimization	Recently, 3D generative models have shown promising performances in structure-based drug design by learning to generate ligands given target binding sites. However, only modeling the target-ligand distribution can hardly fulfill one of the main goals in drug discovery -- designing novel ligands with desired properties,...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	439,802
1312.5663	k-Sparse Autoencoders	Recently, it has been observed that when representations are learnt in a way that encourages sparsity, improved performance is obtained on classification tasks. These methods involve combinations of activation functions, sampling steps and different kinds of penalties. To investigate the effectiveness of sparsity by it...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	29,251
2408.03528	Exploring the extent of similarities in software failures across industries using LLMs	The rapid evolution of software development necessitates enhanced safety measures. Extracting information about software failures from companies is becoming increasingly more available through news articles. This research utilizes the Failure Analysis Investigation with LLMs (FAIL) model to extract industry-specific ...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	479,053
1904.09339	Continuous-Time Birth-Death MCMC for Bayesian Regression Tree Models	Decision trees are flexible models that are well suited for many statistical regression problems. In a Bayesian framework for regression trees, Markov Chain Monte Carlo (MCMC) search algorithms are required to generate samples of tree models according to their posterior probabilities. The critical component of such an ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	128,345
2410.19341	Context-Based Visual-Language Place Recognition	In vision-based robot localization and SLAM, Visual Place Recognition (VPR) is essential. This paper addresses the problem of VPR, which involves accurately recognizing the location corresponding to a given query image. A popular approach to vision-based place recognition relies on low-level visual features. Despite si...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	502,270
2312.03795	AnimatableDreamer: Text-Guided Non-rigid 3D Model Generation and Reconstruction with Canonical Score Distillation	Advances in 3D generation have facilitated sequential 3D model generation (a.k.a 4D generation), yet its application for animatable objects with large motion remains scarce. Our work proposes AnimatableDreamer, a text-to-4D generation framework capable of generating diverse categories of non-rigid objects on skeletons ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	413,437
2003.07761	CycleISP: Real Image Restoration via Improved Data Synthesis	The availability of large-scale datasets has helped unleash the true potential of deep convolutional neural networks (CNNs). However, for the single-image denoising problem, capturing a real dataset is an unacceptably expensive and cumbersome procedure. Consequently, image denoising algorithms are mostly developed and ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	168,535
2211.00745	Self-supervised Physics-based Denoising for Computed Tomography	Computed Tomography (CT) imposes risk on the patients due to its inherent X-ray radiation, stimulating the development of low-dose CT (LDCT) imaging methods. Lowering the radiation dose reduces the health risks but leads to noisier measurements, which decreases the tissue contrast and causes artifacts in CT images. Ult...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	327,980
2411.10476	Efficient Denoising Method to Improve The Resolution of Satellite Images	Satellites are widely used to estimate and monitor ground cover, providing critical information to address the challenges posed by climate change. High-resolution satellite images help to identify smaller features on the ground and classification of ground cover types. Small satellites have become very popular recently...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	508,653
2203.14498	EnCBP: A New Benchmark Dataset for Finer-Grained Cultural Background Prediction in English	While cultural backgrounds have been shown to affect linguistic expressions, existing natural language processing (NLP) research on culture modeling is overly coarse-grained and does not examine cultural differences among speakers of the same language. To address this problem and augment NLP models with cultural backgr...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	288,032
2502.11481	Variable-frame CNNLSTM for Breast Nodule Classification using Ultrasound Videos	The intersection of medical imaging and artificial intelligence has become an important research direction in intelligent medical treatment, particularly in the analysis of medical images using deep learning for clinical diagnosis. Despite the advances, existing keyframe classification methods lack extraction of time s...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	534,398
1512.08580	A Simple Baseline for Travel Time Estimation using Large-Scale Trip Data	The increased availability of large-scale trajectory data around the world provides rich information for the study of urban dynamics. For example, New York City Taxi Limousine Commission regularly releases source-destination information about trips in the taxis they regulate. Taxi data provide information about traffic...	false	false	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	50,530
2408.06010	DEEPTalk: Dynamic Emotion Embedding for Probabilistic Speech-Driven 3D Face Animation	Speech-driven 3D facial animation has garnered lots of attention thanks to its broad range of applications. Despite recent advancements in achieving realistic lip motion, current methods fail to capture the nuanced emotional undertones conveyed through speech and produce monotonous facial motion. These limitations resu...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	480,042
2412.08389	SweetieChat: A Strategy-Enhanced Role-playing Framework for Diverse Scenarios Handling Emotional Support Agent	Large Language Models (LLMs) have demonstrated promising potential in providing empathetic support during interactions. However, their responses often become verbose or overly formulaic, failing to adequately address the diverse emotional support needs of real-world scenarios. To tackle this challenge, we propose an in...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	516,066
2407.18357	Needle Segmentation Using GAN: Restoring Thin Instrument Visibility in Robotic Ultrasound	Ultrasound-guided percutaneous needle insertion is a standard procedure employed in both biopsy and ablation in clinical practices. However, due to the complex interaction between tissue and instrument, the needle may deviate from the in-plane view, resulting in a lack of close monitoring of the percutaneous needle. To...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	476,330

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