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
2105.03917	Combining Time-Dependent Force Perturbations in Robot-Assisted Surgery Training	Teleoperated robot-assisted minimally-invasive surgery (RAMIS) offers many advantages over open surgery. However, there are still no guidelines for training skills in RAMIS. Motor learning theories have the potential to improve the design of RAMIS training but they are based on simple movements that do not resemble the...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	234,318
1911.06164	Learning Model Bias	In this paper the problem of {\em learning} appropriate domain-specific bias is addressed. It is shown that this can be achieved by learning many related tasks from the same domain, and a theorem is given bounding the number tasks that must be learnt. A corollary of the theorem is that if the tasks are known to possess...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	153,466
2105.07043	Post-processing Multi-Model Medium-Term Precipitation Forecasts Using Convolutional Neural Networks	The goal of this study was to improve the post-processing of precipitation forecasts using convolutional neural networks (CNNs). Instead of post-processing forecasts on a per-pixel basis, as is usually done when employing machine learning in meteorological post-processing, input forecast images were combined and transf...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	235,295
2101.09509	Short-term daily precipitation forecasting with seasonally-integrated autoencoder	Short-term precipitation forecasting is essential for planning of human activities in multiple scales, ranging from individuals' planning, urban management to flood prevention. Yet the short-term atmospheric dynamics are highly nonlinear that it cannot be easily captured with classical time series models. On the other ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	216,621
2404.18118	Finite-time Safety and Reach-avoid Verification of Stochastic Discrete-time Systems	This paper studies finite-time safety and reach-avoid verification for stochastic discrete-time dynamical systems. The aim is to ascertain lower and upper bounds of the probability that, within a predefined finite-time horizon, a system starting from an initial state in a safe set will either exit the safe set (safety ...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	450,134
1705.00454	Autocorrelation Function for Dispersion-Free Fiber Channels with Distributed Amplification	Optical fiber signals with high power exhibit spectral broadening that seems to limit capacity. To study spectral broadening, the autocorrelation function of the output signal given the input signal is derived for a simplified fiber model that has zero dispersion, distributed optical amplification (OA), and idealized s...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	72,684
2411.01647	Optical Flow Representation Alignment Mamba Diffusion Model for Medical Video Generation	Medical video generation models are expected to have a profound impact on the healthcare industry, including but not limited to medical education and training, surgical planning, and simulation. Current video diffusion models typically build on image diffusion architecture by incorporating temporal operations (such as ...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	505,168
2007.14184	A Commentary on the Unsupervised Learning of Disentangled Representations	The goal of the unsupervised learning of disentangled representations is to separate the independent explanatory factors of variation in the data without access to supervision. In this paper, we summarize the results of Locatello et al., 2019, and focus on their implications for practitioners. We discuss the theoretica...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	189,327
2106.05215	A machine learning pipeline for aiding school identification from child trafficking images	Child trafficking in a serious problem around the world. Every year there are more than 4 million victims of child trafficking around the world, many of them for the purposes of child sexual exploitation. In collaboration with UK Police and a non-profit focused on child abuse prevention, Global Emancipation Network, we...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	240,011
1708.02179	Self-supervised Learning of Pose Embeddings from Spatiotemporal Relations in Videos	Human pose analysis is presently dominated by deep convolutional networks trained with extensive manual annotations of joint locations and beyond. To avoid the need for expensive labeling, we exploit spatiotemporal relations in training videos for self-supervised learning of pose embeddings. The key idea is to combine ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	78,533
2307.16707	Bi-Level Image-Guided Ergodic Exploration with Applications to Planetary Rovers	We present a method for image-guided exploration for mobile robotic systems. Our approach extends ergodic exploration methods, a recent exploration approach that prioritizes complete coverage of a space, with the use of a learned image classifier that automatically detects objects and updates an information map to guid...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	382,706
2411.05122	Socially Assistive Robots: A Technological Approach to Emotional Support	In today's high-pressure and isolated society, the demand for emotional support has surged, necessitating innovative solutions. Socially Assistive Robots (SARs) offer a technological approach to providing emotional assistance by leveraging advanced robotics, artificial intelligence, and sensor technologies. This study ...	true	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	506,554
2502.10475	X-SG$^2$S: Safe and Generalizable Gaussian Splatting with X-dimensional Watermarks	3D Gaussian Splatting (3DGS) has been widely used in 3D reconstruction and 3D generation. Training to get a 3DGS scene often takes a lot of time and resources and even valuable inspiration. The increasing amount of 3DGS digital asset have brought great challenges to the copyright protection. However, it still lacks pro...	false	false	false	false	true	false	false	false	false	false	false	true	true	false	false	false	false	false	533,903
2502.06208	Product gales and Finite state dimension	In this work, we introduce the notion of product gales, which is the modification of an $s$-gale such that $k$ separate bets can be placed at each symbol. The product of the bets placed are taken into the capital function of the product-gale. We show that Hausdorff dimension can be characterised using product gales. ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	531,985
2309.10702	Formal Abstraction of General Stochastic Systems via Noise Partitioning	Verifying the performance of safety-critical, stochastic systems with complex noise distributions is difficult. We introduce a general procedure for the finite abstraction of nonlinear stochastic systems with non-standard (e.g., non-affine, non-symmetric, non-unimodal) noise distributions for verification purposes. The...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	393,120
1809.04640	Jump to better conclusions: SCAN both left and right	Lake and Baroni (2018) recently introduced the SCAN data set, which consists of simple commands paired with action sequences and is intended to test the strong generalization abilities of recurrent sequence-to-sequence models. Their initial experiments suggested that such models may fail because they lack the ability t...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	107,615
1603.07919	Global sensitivity analysis with 2d hydraulic codes: applied protocol and practical tool	Global Sensitivity Analysis (GSA) methods are useful tools to rank input parameters uncertainties regarding their impact on result variability. In practice, such type of approach is still at an exploratory level for studies relying on 2D Shallow Water Equations (SWE) codes as GSA requires specific tools and deals with ...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	53,687
2411.01706	Investigating Large Language Models for Complex Word Identification in Multilingual and Multidomain Setups	Complex Word Identification (CWI) is an essential step in the lexical simplification task and has recently become a task on its own. Some variations of this binary classification task have emerged, such as lexical complexity prediction (LCP) and complexity evaluation of multi-word expressions (MWE). Large language mode...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	505,187
1905.06482	Deep Session Interest Network for Click-Through Rate Prediction	Click-Through Rate (CTR) prediction plays an important role in many industrial applications, such as online advertising and recommender systems. How to capture users' dynamic and evolving interests from their behavior sequences remains a continuous research topic in the CTR prediction. However, most existing studies ov...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	131,004
2301.00007	Selected aspects of complex, hypercomplex and fuzzy neural networks	This short report reviews the current state of the research and methodology on theoretical and practical aspects of Artificial Neural Networks (ANN). It was prepared to gather state-of-the-art knowledge needed to construct complex, hypercomplex and fuzzy neural networks. The report reflects the individual interests o...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	338,767
2403.07379	Hallmarks of Optimization Trajectories in Neural Networks: Directional Exploration and Redundancy	We propose a fresh take on understanding the mechanisms of neural networks by analyzing the rich directional structure of optimization trajectories, represented by their pointwise parameters. Towards this end, we introduce some natural notions of the complexity of optimization trajectories, both qualitative and quantit...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	436,862
2301.11630	Joint Geometry and Attribute Upsampling of Point Clouds Using Frequency-Selective Models with Overlapped Support	With the increasing demand of capturing our environment in three-dimensions for AR/ VR applications and autonomous driving among others, the importance of high-resolution point clouds rises. As the capturing process is a complex task, point cloud upsampling is often desired. We propose Frequency-Selective Upsampling (F...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	342,221
1907.05023	Micro-expression Action Unit Detection with Spatio-temporal Adaptive Pooling	Action Unit (AU) detection plays an important role for facial expression recognition. To the best of our knowledge, there is little research about AU analysis for micro-expressions. In this paper, we focus on AU detection in micro-expressions. Microexpression AU detection is challenging due to the small quantity of mic...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	138,261
2411.18877	Swarm Intelligence-Driven Client Selection for Federated Learning in Cybersecurity applications	This study addresses a critical gap in the literature regarding the use of Swarm Intelligence Optimization (SI) algorithms for client selection in Federated Learning (FL), with a focus on cybersecurity applications. Existing research primarily explores optimization techniques for centralized machine learning, leaving t...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	512,035
1906.11362	Interactive Physics-Inspired Traffic Congestion Management	This paper proposes a new physics-based approach to effectively control congestion in a network of interconnected roads (NOIR). The paper integrates mass flow conservation and diffusion-based dynamics to model traffic coordination in a NOIR. The mass conservation law is used to model the traffic density dynamics across...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	136,645
2103.13313	In-flight positional and energy use data set of a DJI Matrice 100 quadcopter for small package delivery	We autonomously direct a small quadcopter package delivery Uncrewed Aerial Vehicle (UAV) or "drone" to take off, fly a specified route, and land for a total of 209 flights while varying a set of operational parameters. The vehicle was equipped with onboard sensors, including GPS, IMU, voltage and current sensors, and a...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	226,453
2312.05290	Noise Adaptor in Spiking Neural Networks	Recent strides in low-latency spiking neural network (SNN) algorithms have drawn significant interest, particularly due to their event-driven computing nature and fast inference capability. One of the most efficient ways to construct a low-latency SNN is by converting a pre-trained, low-bit artificial neural network (A...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	414,027
1906.09955	A Comparative Review of Recent Kinect-based Action Recognition Algorithms	Video-based human action recognition is currently one of the most active research areas in computer vision. Various research studies indicate that the performance of action recognition is highly dependent on the type of features being extracted and how the actions are represented. Since the release of the Kinect camera...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	136,319
2008.00965	End-to-end Full Projector Compensation	Full projector compensation aims to modify a projector input image to compensate for both geometric and photometric disturbance of the projection surface. Traditional methods usually solve the two parts separately and may suffer from suboptimal solutions. In this paper, we propose the first end-to-end differentiable so...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	190,167
2303.09058	SVDE: Scalable Value-Decomposition Exploration for Cooperative Multi-Agent Reinforcement Learning	Value-decomposition methods, which reduce the difficulty of a multi-agent system by decomposing the joint state-action space into local observation-action spaces, have become popular in cooperative multi-agent reinforcement learning (MARL). However, value-decomposition methods still have the problems of tremendous samp...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	351,889
1908.06938	Encoder-Agnostic Adaptation for Conditional Language Generation	Large pretrained language models have changed the way researchers approach discriminative natural language understanding tasks, leading to the dominance of approaches that adapt a pretrained model for arbitrary downstream tasks. However it is an open-question how to use similar techniques for language generation. Early...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	142,157
2202.09459	Interactive Visual Pattern Search on Graph Data via Graph Representation Learning	Graphs are a ubiquitous data structure to model processes and relations in a wide range of domains. Examples include control-flow graphs in programs and semantic scene graphs in images. Identifying subgraph patterns in graphs is an important approach to understanding their structural properties. We propose a visual ana...	true	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	281,197
2108.03648	From Voxel to Point: IoU-guided 3D Object Detection for Point Cloud with Voxel-to-Point Decoder	In this paper, we present an Intersection-over-Union (IoU) guided two-stage 3D object detector with a voxel-to-point decoder. To preserve the necessary information from all raw points and maintain the high box recall in voxel based Region Proposal Network (RPN), we propose a residual voxel-to-point decoder to extract t...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	249,737
1910.00699	Decision Automation for Electric Power Network Recovery	Critical infrastructure systems such as electric power networks, water networks, and transportation systems play a major role in the welfare of any community. In the aftermath of disasters, their recovery is of paramount importance; orderly and efficient recovery involves the assignment of limited resources (a combinat...	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	false	false	147,735
1810.11246	Energy regenerative damping in variable impedance actuators for long-term robotic deployment	Energy efficiency is a crucial issue towards longterm deployment of compliant robots in the real world. In the context of variable impedance actuators (VIAs), one of the main focuses has been on improving energy efficiency through reduction of energy consumption. However, the harvesting of dissipated energy in such sys...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	111,462
1203.2839	Square-Cut: A Segmentation Algorithm on the Basis of a Rectangle Shape	We present a rectangle-based segmentation algorithm that sets up a graph and performs a graph cut to separate an object from the background. However, graph-based algorithms distribute the graph's nodes uniformly and equidistantly on the image. Then, a smoothness term is added to force the cut to prefer a particular sha...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	14,859
2206.09591	Domain-Adaptive Text Classification with Structured Knowledge from Unlabeled Data	Domain adaptive text classification is a challenging problem for the large-scale pretrained language models because they often require expensive additional labeled data to adapt to new domains. Existing works usually fails to leverage the implicit relationships among words across domains. In this paper, we propose a no...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	303,630
2502.05714	Proving the Coding Interview: A Benchmark for Formally Verified Code Generation	We introduce the Formally Verified Automated Programming Progress Standards, or FVAPPS, a benchmark of 4715 samples for writing programs and proving their correctness, the largest formal verification benchmark, including 1083 curated and quality controlled samples. Previously, APPS provided a benchmark and dataset for ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	true	531,740
2405.11647	Hummer: Towards Limited Competitive Preference Dataset	Preference datasets are essential for incorporating human preferences into pre-trained language models, playing a key role in the success of Reinforcement Learning from Human Feedback. However, these datasets often demonstrate conflicting alignment objectives, leading to increased vulnerability to jailbreak attacks and...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	455,224
1308.1009	Sign Stable Projections, Sign Cauchy Projections and Chi-Square Kernels	The method of stable random projections is popular for efficiently computing the Lp distances in high dimension (where 0<p<=2), using small space. Because it adopts nonadaptive linear projections, this method is naturally suitable when the data are collected in a dynamic streaming fashion (i.e., turnstile data streams)...	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	true	26,270
2407.18874	Engaging with Children's Artwork in Mixed Visual-Ability Families	We present two studies exploring how blind or low-vision (BLV) family members engage with their sighted children's artwork, strategies to support understanding and interpretation, and the potential role of technology, such as AI, therein. Our first study involved 14 BLV individuals, and the second included five groups ...	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	476,545
1909.02768	Pairwise Learning to Rank by Neural Networks Revisited: Reconstruction, Theoretical Analysis and Practical Performance	We present a pairwise learning to rank approach based on a neural net, called DirectRanker, that generalizes the RankNet architecture. We show mathematically that our model is reflexive, antisymmetric, and transitive allowing for simplified training and improved performance. Experimental results on the LETOR MSLR-WEB10...	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	144,282
2306.05270	Overview of the Problem List Summarization (ProbSum) 2023 Shared Task on Summarizing Patients' Active Diagnoses and Problems from Electronic Health Record Progress Notes	The BioNLP Workshop 2023 initiated the launch of a shared task on Problem List Summarization (ProbSum) in January 2023. The aim of this shared task is to attract future research efforts in building NLP models for real-world diagnostic decision support applications, where a system generating relevant and accurate diagno...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	372,119
2012.11327	Collaborative residual learners for automatic icd10 prediction using prescribed medications	Clinical coding is an administrative process that involves the translation of diagnostic data from episodes of care into a standard code format such as ICD10. It has many critical applications such as billing and aetiology research. The automation of clinical coding is very challenging due to data sparsity, low interop...	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	212,603
2408.05485	Contrast, Imitate, Adapt: Learning Robotic Skills From Raw Human Videos	Learning robotic skills from raw human videos remains a non-trivial challenge. Previous works tackled this problem by leveraging behavior cloning or learning reward functions from videos. Despite their remarkable performances, they may introduce several issues, such as the necessity for robot actions, requirements for ...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	479,809
1901.05727	Sparse Non-Negative Recovery from Biased Subgaussian Measurements using NNLS	We investigate non-negative least squares (NNLS) for the recovery of sparse non-negative vectors from noisy linear and biased measurements. We build upon recent results from [1] showing that for matrices whose row-span intersects the positive orthant, the nullspace property (NSP) implies compressed sensing recovery gua...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	118,843
2010.15582	Improving Accuracy of Federated Learning in Non-IID Settings	Federated Learning (FL) is a decentralized machine learning protocol that allows a set of participating agents to collaboratively train a model without sharing their data. This makes FL particularly suitable for settings where data privacy is desired. However, it has been observed that the performance of FL is closely ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	true	203,814
1712.05969	Learning a Virtual Codec Based on Deep Convolutional Neural Network to Compress Image	Although deep convolutional neural network has been proved to efficiently eliminate coding artifacts caused by the coarse quantization of traditional codec, it's difficult to train any neural network in front of the encoder for gradient's back-propagation. In this paper, we propose an end-to-end image compression frame...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	86,803
2010.06235	Robust Two-Stream Multi-Feature Network for Driver Drowsiness Detection	Drowsiness driving is a major cause of traffic accidents and thus numerous previous researches have focused on driver drowsiness detection. Many drive relevant factors have been taken into consideration for fatigue detection and can lead to high precision, but there are still several serious constraints, such as most e...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	200,417
2410.19319	Fully First-Order Methods for Decentralized Bilevel Optimization	This paper focuses on decentralized stochastic bilevel optimization (DSBO) where agents only communicate with their neighbors. We propose Decentralized Stochastic Gradient Descent and Ascent with Gradient Tracking (DSGDA-GT), a novel algorithm that only requires first-order oracles that are much cheaper than second-ord...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	502,264
2106.03793	Pointwise visual field estimation from optical coherence tomography in glaucoma: a structure-function analysis using deep learning	Background/Aims: Standard Automated Perimetry (SAP) is the gold standard to monitor visual field (VF) loss in glaucoma management, but is prone to intra-subject variability. We developed and validated a deep learning (DL) regression model that estimates pointwise and overall VF loss from unsegmented optical coherence t...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	239,455
1507.05228	Diffusion Adaptation over Multi-Agent Networks with Wireless Link Impairments	We study the performance of diffusion least-mean-square algorithms for distributed parameter estimation in multi-agent networks when nodes exchange information over wireless communication links. Wireless channel impairments, such as fading and path-loss, adversely affect the exchanged data and cause instability and per...	false	false	false	false	false	false	false	false	false	true	true	false	false	false	false	false	false	true	45,256
1601.04568	Content Aware Neural Style Transfer	This paper presents a content-aware style transfer algorithm for paintings and photos of similar content using pre-trained neural network, obtaining better results than the previous work. In addition, the numerical experiments show that the style pattern and the content information is not completely separated by neural...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	51,033
1301.3192	Matrix Approximation under Local Low-Rank Assumption	Matrix approximation is a common tool in machine learning for building accurate prediction models for recommendation systems, text mining, and computer vision. A prevalent assumption in constructing matrix approximations is that the partially observed matrix is of low-rank. We propose a new matrix approximation model w...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	21,067
1604.04999	A Band-independent Variable Step Size Proportionate Normalized Subband Adaptive Filter Algorithm	Proportionate-type normalized suband adaptive filter (PNSAF-type) algorithms are very attractive choices for echo cancellation. To further obtain both fast convergence rate and low steady-state error, in this paper, a variable step size (VSS) version of the presented improved PNSAF (IPNSAF) algorithm is proposed by min...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	54,745
2403.17530	Boosting Few-Shot Learning with Disentangled Self-Supervised Learning and Meta-Learning for Medical Image Classification	Background and objective: Employing deep learning models in critical domains such as medical imaging poses challenges associated with the limited availability of training data. We present a strategy for improving the performance and generalization capabilities of models trained in low-data regimes. Methods: The propose...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	441,503
1505.04260	The color of smiling: computational synaesthesia of facial expressions	This note gives a preliminary account of the transcoding or rechanneling problem between different stimuli as it is of interest for the natural interaction or affective computing fields. By the consideration of a simple example, namely the color response of an affective lamp to a sensed facial expression, we frame the ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	43,169
2304.03153	Zero-Shot Next-Item Recommendation using Large Pretrained Language Models	Large language models (LLMs) have achieved impressive zero-shot performance in various natural language processing (NLP) tasks, demonstrating their capabilities for inference without training examples. Despite their success, no research has yet explored the potential of LLMs to perform next-item recommendations in the ...	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	356,687
1801.00584	Co-Clustering via Information-Theoretic Markov Aggregation	We present an information-theoretic cost function for co-clustering, i.e., for simultaneous clustering of two sets based on similarities between their elements. By constructing a simple random walk on the corresponding bipartite graph, our cost function is derived from a recently proposed generalized framework for info...	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	87,592
2210.03104	Distributionally Adaptive Meta Reinforcement Learning	Meta-reinforcement learning algorithms provide a data-driven way to acquire policies that quickly adapt to many tasks with varying rewards or dynamics functions. However, learned meta-policies are often effective only on the exact task distribution on which they were trained and struggle in the presence of distribution...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	321,898
2303.00521	Quality-aware Pre-trained Models for Blind Image Quality Assessment	Blind image quality assessment (BIQA) aims to automatically evaluate the perceived quality of a single image, whose performance has been improved by deep learning-based methods in recent years. However, the paucity of labeled data somewhat restrains deep learning-based BIQA methods from unleashing their full potential....	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	348,627
2004.03378	Error-Corrected Margin-Based Deep Cross-Modal Hashing for Facial Image Retrieval	Cross-modal hashing facilitates mapping of heterogeneous multimedia data into a common Hamming space, which can beutilized for fast and flexible retrieval across different modalities. In this paper, we propose a novel cross-modal hashingarchitecture-deep neural decoder cross-modal hashing (DNDCMH), which uses a binary ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	171,541
2409.16125	Analyzing Probabilistic Methods for Evaluating Agent Capabilities	To mitigate risks from AI systems, we need to assess their capabilities accurately. This is especially difficult in cases where capabilities are only rarely displayed. Phuong et al. propose two methods that aim to obtain better estimates of the probability of an AI agent successfully completing a given task. The milest...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	491,215
1910.04297	Online Simultaneous Semi-Parametric Dynamics Model Learning	Accurate models of robots' dynamics are critical for control, stability, motion optimization, and interaction. Semi-Parametric approaches to dynamics learning combine physics-based Parametric models with unstructured Non-Parametric regression with the hope to achieve both accuracy and generalizablity. In this paper we ...	false	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	148,725
2404.04612	Spectral Graph Pruning Against Over-Squashing and Over-Smoothing	Message Passing Graph Neural Networks are known to suffer from two problems that are sometimes believed to be diametrically opposed: over-squashing and over-smoothing. The former results from topological bottlenecks that hamper the information flow from distant nodes and are mitigated by spectral gap maximization, prim...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	444,718
1406.6778	Performance Comparison of Two Streaming Data Clustering Algorithms	The weighted fuzzy c-mean clustering algorithm and weighted fuzzy c-mean-adaptive cluster number are extension of traditional fuzzy c-mean Algorithm to stream data clustering algorithm.	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	34,152
1406.2746	Are 140 Characters Enough? A Large-Scale Linkability Study of Tweets	Microblogging is a very popular Internet activity that informs and entertains great multitudes of people world-wide via quickly and scalably disseminated terse messages containing all kinds of newsworthy utterances. Even though microblogging is neither designed nor meant to emphasize privacy, numerous contributors hide...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	true	33,783
2305.19352	LLM-BRAIn: AI-driven Fast Generation of Robot Behaviour Tree based on Large Language Model	This paper presents a novel approach in autonomous robot control, named LLM-BRAIn, that makes possible robot behavior generation, based on operator's commands. LLM-BRAIn is a transformer-based Large Language Model (LLM) fine-tuned from Stanford Alpaca 7B model to generate robot behavior tree (BT) from the text descript...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	369,484
2307.16670	Conditioning Generative Latent Optimization for Sparse-View CT Image Reconstruction	Computed Tomography (CT) is a prominent example of Imaging Inverse Problem highlighting the unrivaled performances of data-driven methods in degraded measurements setups like sparse X-ray projections. Although a significant proportion of deep learning approaches benefit from large supervised datasets, they cannot gener...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	382,688
2210.09881	Random Orthogonalization for Federated Learning in Massive MIMO Systems	We propose a novel communication design, termed random orthogonalization, for federated learning (FL) in a massive multiple-input and multiple-output (MIMO) wireless system. The key novelty of random orthogonalization comes from the tight coupling of FL and two unique characteristics of massive MIMO -- channel hardenin...	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	324,703
1811.10275	Rejoinder for "Probabilistic Integration: A Role in Statistical Computation?"	This article is the rejoinder for the paper "Probabilistic Integration: A Role in Statistical Computation?" to appear in Statistical Science with discussion. We would first like to thank the reviewers and many of our colleagues who helped shape this paper, the editor for selecting our paper for discussion, and of cours...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	114,456
2305.08524	Measuring Consistency in Text-based Financial Forecasting Models	Financial forecasting has been an important and active area of machine learning research, as even the most modest advantage in predictive accuracy can be parlayed into significant financial gains. Recent advances in natural language processing (NLP) bring the opportunity to leverage textual data, such as earnings repor...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	364,315
2007.09791	E$^2$Net: An Edge Enhanced Network for Accurate Liver and Tumor Segmentation on CT Scans	Developing an effective liver and liver tumor segmentation model from CT scans is very important for the success of liver cancer diagnosis, surgical planning and cancer treatment. In this work, we propose a two-stage framework for 2D liver and tumor segmentation. The first stage is a coarse liver segmentation network, ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	188,069
2406.02560	Less Peaky and More Accurate CTC Forced Alignment by Label Priors	Connectionist temporal classification (CTC) models are known to have peaky output distributions. Such behavior is not a problem for automatic speech recognition (ASR), but it can cause inaccurate forced alignments (FA), especially at finer granularity, e.g., phoneme level. This paper aims at alleviating the peaky behav...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	460,829
1909.06057	Strategic Inference with a Single Private Sample	Motivated by applications in cyber security, we develop a simple game model for describing how a learning agent's private information influences an observing agent's inference process. The model describes a situation in which one of the agents (attacker) is deciding which of two targets to attack, one with a known rewa...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	true	145,281
2501.14837	A Semiparametric Bayesian Method for Instrumental Variable Analysis with Partly Interval-Censored Time-to-Event Outcome	This paper develops a semiparametric Bayesian instrumental variable analysis method for estimating the causal effect of an endogenous variable when dealing with unobserved confounders and measurement errors with partly interval-censored time-to-event data, where event times are observed exactly for some subjects but le...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	527,297
2302.09119	A Review on Generative Adversarial Networks for Data Augmentation in Person Re-Identification Systems	Interest in automatic people re-identification systems has significantly grown in recent years, mainly for developing surveillance and smart shops software. Due to the variability in person posture, different lighting conditions, and occluded scenarios, together with the poor quality of the images obtained by different...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	346,293
1204.0165	Analytical Models for Power Networks: The case of the Western US and ERCOT grids	The topological structure of the power grid plays a key role in the reliable delivery of electricity and price settlement in the electricity market. Incorporation of new energy sources and loads into the grid over time has led to its structural and geographical expansion and can affect its stable operation. This paper ...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	15,216
2407.15352	MAVEN-Fact: A Large-scale Event Factuality Detection Dataset	Event Factuality Detection (EFD) task determines the factuality of textual events, i.e., classifying whether an event is a fact, possibility, or impossibility, which is essential for faithfully understanding and utilizing event knowledge. However, due to the lack of high-quality large-scale data, event factuality detec...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	475,147
2206.06157	Towards Target High-Utility Itemsets	For applied intelligence, utility-driven pattern discovery algorithms can identify insightful and useful patterns in databases. However, in these techniques for pattern discovery, the number of patterns can be huge, and the user is often only interested in a few of those patterns. Hence, targeted high-utility itemset m...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	true	false	302,274
1508.05699	Detecting and Preventing "Multiple-Account" Cheating in Massive Open Online Courses	We describe a cheating strategy enabled by the features of massive open online courses (MOOCs) and detectable by virtue of the sophisticated data systems that MOOCs provide. The strategy, Copying Answers using Multiple Existences Online (CAMEO), involves a user who gathers solutions to assessment questions using a "har...	false	false	false	true	false	false	false	false	false	false	false	false	false	true	false	false	false	false	46,250
1504.01151	Design method for an anthropomorphic hand able to gesture and grasp	This paper presents a numerical method to conceive and design the kinematic model of an anthropomorphic robotic hand used for gesturing and grasping. In literature, there are few numerical methods for the finger placement of human-inspired robotic hands. In particular, there are no numerical methods, for the thumb plac...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	41,777
2109.06241	Incremental Abstraction in Distributed Probabilistic SLAM Graphs	Scene graphs represent the key components of a scene in a compact and semantically rich way, but are difficult to build during incremental SLAM operation because of the challenges of robustly identifying abstract scene elements and optimising continually changing, complex graphs. We present a distributed, graph-based S...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	255,083
2304.12454	Benchmark tasks for Quality-Diversity applied to Uncertain domains	While standard approaches to optimisation focus on producing a single high-performing solution, Quality-Diversity (QD) algorithms allow large diverse collections of such solutions to be found. If QD has proven promising across a large variety of domains, it still struggles when faced with uncertain domains, where quant...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	360,215
2312.07887	Learn or Recall? Revisiting Incremental Learning with Pre-trained Language Models	Incremental Learning (IL) has been a long-standing problem in both vision and Natural Language Processing (NLP) communities. In recent years, as Pre-trained Language Models (PLMs) have achieved remarkable progress in various NLP downstream tasks, utilizing PLMs as backbones has become a common practice in recent resear...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	415,105
2409.07107	End-to-End and Highly-Efficient Differentiable Simulation for Robotics	Over the past few years, robotics simulators have largely improved in efficiency and scalability, enabling them to generate years of simulated data in a few hours. Yet, efficiently and accurately computing the simulation derivatives remains an open challenge, with potentially high gains on the convergence speed of rein...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	487,387
2501.08046	Building Symbiotic AI: Reviewing the AI Act for a Human-Centred, Principle-Based Framework	Artificial Intelligence (AI) spreads quickly as new technologies and services take over modern society. The need to regulate AI design, development, and use is strictly necessary to avoid unethical and potentially dangerous consequences to humans. The European Union (EU) has released a new legal framework, the AI Act, ...	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	524,608
1710.09979	Stochastic Conjugate Gradient Algorithm with Variance Reduction	Conjugate gradient (CG) methods are a class of important methods for solving linear equations and nonlinear optimization problems. In this paper, we propose a new stochastic CG algorithm with variance reduction and we prove its linear convergence with the Fletcher and Reeves method for strongly convex and smooth functi...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	83,291
2008.08574	Every Pixel Matters: Center-aware Feature Alignment for Domain Adaptive Object Detector	A domain adaptive object detector aims to adapt itself to unseen domains that may contain variations of object appearance, viewpoints or backgrounds. Most existing methods adopt feature alignment either on the image level or instance level. However, image-level alignment on global features may tangle foreground/backgro...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	192,458
1801.10527	Analysing Collective Behaviour in Temporal Networks Using Event Graphs and Temporal Motifs	Historically studies of behaviour on networks have focused on the behaviour of individuals (node-based) or on the aggregate behaviour of the entire network. We propose a new method to decompose a temporal network into macroscale components and to analyse the behaviour of these components, or collectives of nodes, acros...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	89,312
1109.1059	C-Rank: A Link-based Similarity Measure for Scientific Literature Databases	As the number of people who use scientific literature databases grows, the demand for literature retrieval services has been steadily increased. One of the most popular retrieval services is to find a set of papers similar to the paper under consideration, which requires a measure that computes similarities between pap...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	true	11,984
2402.07268	Highly Accurate Disease Diagnosis and Highly Reproducible Biomarker Identification with PathFormer	Biomarker identification is critical for precise disease diagnosis and understanding disease pathogenesis in omics data analysis, like using fold change and regression analysis. Graph neural networks (GNNs) have been the dominant deep learning model for analyzing graph-structured data. However, we found two major limit...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	428,638
1802.05335	Multimodal Generative Models for Scalable Weakly-Supervised Learning	Multiple modalities often co-occur when describing natural phenomena. Learning a joint representation of these modalities should yield deeper and more useful representations. Previous generative approaches to multi-modal input either do not learn a joint distribution or require additional computation to handle missing ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	90,417
2306.10287	Linearly-scalable learning of smooth low-dimensional patterns with permutation-aided entropic dimension reduction	In many data science applications, the objective is to extract appropriately-ordered smooth low-dimensional data patterns from high-dimensional data sets. This is challenging since common sorting algorithms are primarily aiming at finding monotonic orderings in low-dimensional data, whereas typical dimension reduction ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	374,169
2002.08331	Towards a Complete Pipeline for Segmenting Nuclei in Feulgen-Stained Images	Cervical cancer is the second most common cancer type in women around the world. In some countries, due to non-existent or inadequate screening, it is often detected at late stages, making standard treatment options often absent or unaffordable. It is a deadly disease that could benefit from early detection approaches....	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	164,726
1807.07364	Revisiting Cross Modal Retrieval	This paper proposes a cross-modal retrieval system that leverages on image and text encoding. Most multimodal architectures employ separate networks for each modality to capture the semantic relationship between them. However, in our work image-text encoding can achieve comparable results in terms of cross-modal retrie...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	103,306
1906.00067	OK-VQA: A Visual Question Answering Benchmark Requiring External Knowledge	Visual Question Answering (VQA) in its ideal form lets us study reasoning in the joint space of vision and language and serves as a proxy for the AI task of scene understanding. However, most VQA benchmarks to date are focused on questions such as simple counting, visual attributes, and object detection that do not req...	false	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	133,248
2501.15253	Generalizable Deepfake Detection via Effective Local-Global Feature Extraction	The rapid advancement of GANs and diffusion models has led to the generation of increasingly realistic fake images, posing significant hidden dangers and threats to society. Consequently, deepfake detection has become a pressing issue in today's world. While some existing methods focus on forgery features from either a...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	527,466
2405.19668	AutoBreach: Universal and Adaptive Jailbreaking with Efficient Wordplay-Guided Optimization	Despite the widespread application of large language models (LLMs) across various tasks, recent studies indicate that they are susceptible to jailbreak attacks, which can render their defense mechanisms ineffective. However, previous jailbreak research has frequently been constrained by limited universality, suboptimal...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	458,996
2311.09058	Improving Deep Learning Optimization through Constrained Parameter Regularization	Regularization is a critical component in deep learning. The most commonly used approach, weight decay, applies a constant penalty coefficient uniformly across all parameters. This may be overly restrictive for some parameters, while insufficient for others. To address this, we present Constrained Parameter Regularizat...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	407,974

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