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
2303.16322	FMAS: Fast Multi-Objective SuperNet Architecture Search for Semantic Segmentation	We present FMAS, a fast multi-objective neural architecture search framework for semantic segmentation. FMAS subsamples the structure and pre-trained parameters of DeepLabV3+, without fine-tuning, dramatically reducing training time during search. To further reduce candidate evaluation time, we use a subset of the vali...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	354,820
2110.09253	A Sociotechnical View of Algorithmic Fairness	Algorithmic fairness has been framed as a newly emerging technology that mitigates systemic discrimination in automated decision-making, providing opportunities to improve fairness in information systems (IS). However, based on a state-of-the-art literature review, we argue that fairness is an inherently social concept...	false	false	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	261,745
1909.10180	Path Planning Tolerant to Degraded Locomotion Conditions	Mobile robots, especially those driving outdoors and in unstructured terrain, sometimes suffer from failures and errors in locomotion, like unevenly pressurized or flat tires, loose axes or de-tracked tracks. Those are errors that go unnoticed by the odometry of the robot. Other factors that influence the locomotion pe...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	146,474
2501.18841	Trading Inference-Time Compute for Adversarial Robustness	We conduct experiments on the impact of increasing inference-time compute in reasoning models (specifically OpenAI o1-preview and o1-mini) on their robustness to adversarial attacks. We find that across a variety of attacks, increased inference-time compute leads to improved robustness. In many cases (with important ex...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	528,883
2411.01001	Automated Assessment of Residual Plots with Computer Vision Models	Plotting the residuals is a recommended procedure to diagnose deviations from linear model assumptions, such as non-linearity, heteroscedasticity, and non-normality. The presence of structure in residual plots can be tested using the lineup protocol to do visual inference. There are a variety of conventional residual t...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	504,869
1910.10264	Genetic Programming for Evolving Similarity Functions for Clustering: Representations and Analysis	Clustering is a difficult and widely-studied data mining task, with many varieties of clustering algorithms proposed in the literature. Nearly all algorithms use a similarity measure such as a distance metric (e.g. Euclidean distance) to decide which instances to assign to the same cluster. These similarity measures ar...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	true	false	false	150,434
2212.03692	Transformer-Based Named Entity Recognition for French Using Adversarial Adaptation to Similar Domain Corpora	Named Entity Recognition (NER) involves the identification and classification of named entities in unstructured text into predefined classes. NER in languages with limited resources, like French, is still an open problem due to the lack of large, robust, labelled datasets. In this paper, we propose a transformer-based ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	335,206
2102.06743	Edge Minimizing the Student Conflict Graph	In many schools, courses are given in sections. Prior to timetabling students need to be assigned to individual sections. We give a hybrid approximation sectioning algorithm that minimizes the number of edges (potential conflicts) in the student conflict graph (SCG). We start with a greedy algorithm to obtain a startin...	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	false	false	219,854
2010.10019	Hierarchical Conditional Relation Networks for Multimodal Video Question Answering	Video QA challenges modelers in multiple fronts. Modeling video necessitates building not only spatio-temporal models for the dynamic visual channel but also multimodal structures for associated information channels such as subtitles or audio. Video QA adds at least two more layers of complexity - selecting relevant co...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	201,750
2007.00798	Deliberate Exploration Supports Navigation in Unfamiliar Worlds	To perform tasks well in a new domain, one must first know something about it. This paper reports on a robot controller for navigation through unfamiliar indoor worlds. Based on spatial affordances, it integrates planning with reactive heuristics. Before it addresses specific targets, however, the system deliberately e...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	185,217
2311.04777	Lidar Annotation Is All You Need	In recent years, computer vision has transformed fields such as medical imaging, object recognition, and geospatial analytics. One of the fundamental tasks in computer vision is semantic image segmentation, which is vital for precise object delineation. Autonomous driving represents one of the key areas where computer ...	false	false	false	false	false	false	true	true	false	false	false	true	false	false	false	false	false	false	406,339
2309.16812	SatDM: Synthesizing Realistic Satellite Image with Semantic Layout Conditioning using Diffusion Models	Deep learning models in the Earth Observation domain heavily rely on the availability of large-scale accurately labeled satellite imagery. However, obtaining and labeling satellite imagery is a resource-intensive endeavor. While generative models offer a promising solution to address data scarcity, their potential rema...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	395,507
1605.04785	An Alternative Matting Laplacian	Cutting out and object and estimate its transparency mask is a key task in many applications. We take on the work on closed-form matting by Levin et al., that is used at the core of many matting techniques, and propose an alternative formulation that offers more flexible controls over the matting priors. We also show t...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	55,916
2010.05639	Predicting Clinical Trial Results by Implicit Evidence Integration	Clinical trials provide essential guidance for practicing Evidence-Based Medicine, though often accompanying with unendurable costs and risks. To optimize the design of clinical trials, we introduce a novel Clinical Trial Result Prediction (CTRP) task. In the CTRP framework, a model takes a PICO-formatted clinical tria...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	200,211
1703.03941	A Vision-based Scheme for Kinematic Model Construction of Re-configurable Modular Robots	Re-configurable modular robotic (RMR) systems are advantageous for their reconfigurability and versatility. A new modular robot can be built for a specific task by using modules as building blocks. However, constructing a kinematic model for a newly conceived robot requires significant work. Due to the finite size of m...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	69,806
1205.3676	Consensus of Multi-Agent Networks in the Presence of Adversaries Using Only Local Information	This paper addresses the problem of resilient consensus in the presence of misbehaving nodes. Although it is typical to assume knowledge of at least some nonlocal information when studying secure and fault-tolerant consensus algorithms, this assumption is not suitable for large-scale dynamic networks. To remedy this, w...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	true	16,040
2207.00041	DP$^2$-NILM: A Distributed and Privacy-preserving Framework for Non-intrusive Load Monitoring	Non-intrusive load monitoring (NILM), which usually utilizes machine learning methods and is effective in disaggregating smart meter readings from the household-level into appliance-level consumption, can help analyze electricity consumption behaviours of users and enable practical smart energy and smart grid applicati...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	305,617
1606.09058	A Distributional Semantics Approach to Implicit Language Learning	In the present paper we show that distributional information is particularly important when considering concept availability under implicit language learning conditions. Based on results from different behavioural experiments we argue that the implicit learnability of semantic regularities depends on the degree to whic...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	57,941
2001.03898	Stepwise Model Selection for Sequence Prediction via Deep Kernel Learning	An essential problem in automated machine learning (AutoML) is that of model selection. A unique challenge in the sequential setting is the fact that the optimal model itself may vary over time, depending on the distribution of features and labels available up to each point in time. In this paper, we propose a novel Ba...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	160,094
2211.08517	A Hierarchical Deep Neural Network for Detecting Lines of Codes with Vulnerabilities	Software vulnerabilities, caused by unintentional flaws in source codes, are the main root cause of cyberattacks. Source code static analysis has been used extensively to detect the unintentional defects, i.e. vulnerabilities, introduced into the source codes by software developers. In this paper, we propose a deep lea...	false	false	false	false	true	false	true	false	false	false	false	false	true	false	false	false	false	true	330,657
2407.05419	Multimodal Language Models for Domain-Specific Procedural Video Summarization	Videos serve as a powerful medium to convey ideas, tell stories, and provide detailed instructions, especially through long-format tutorials. Such tutorials are valuable for learning new skills at one's own pace, yet they can be overwhelming due to their length and dense content. Viewers often seek specific information...	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	false	470,970
2005.12987	Skew Gaussian Processes for Classification	Gaussian processes (GPs) are distributions over functions, which provide a Bayesian nonparametric approach to regression and classification. In spite of their success, GPs have limited use in some applications, for example, in some cases a symmetric distribution with respect to its mean is an unreasonable model. This i...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	178,872
1509.01608	Network Structure and Resilience of Mafia Syndicates	In this paper we present the results of the study of Sicilian Mafia organization by using Social Network Analysis. The study investigates the network structure of a Mafia organization, describing its evolution and highlighting its plasticity to interventions targeting membership and its resilience to disruption caused ...	false	false	false	true	false	false	false	false	false	false	false	false	false	true	false	false	false	false	46,629
2408.09588	SynTraC: A Synthetic Dataset for Traffic Signal Control from Traffic Monitoring Cameras	This paper introduces SynTraC, the first public image-based traffic signal control dataset, aimed at bridging the gap between simulated environments and real-world traffic management challenges. Unlike traditional datasets for traffic signal control which aim to provide simplified feature vectors like vehicle counts fr...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	481,503
1207.0554	Proceedings First Workshop on Synthesis	This volume contains the proceedings of the First Workshop on Synthesis (SYNT 2012). The workshop is held is held in Berkeley, California, on June 6th and 7th, as a satellite event to the 24th International Conference on Computer Aided Verification (CAV 2012). SYNT aims at bringing together and providing an open platfo...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	true	17,175
2109.10835	Mapping and Validating a Point Neuron Model on Intel's Neuromorphic Hardware Loihi	Neuromorphic hardware is based on emulating the natural biological structure of the brain. Since its computational model is similar to standard neural models, it could serve as a computational acceleration for research projects in the field of neuroscience and artificial intelligence, including biomedical applications....	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	true	256,760
1906.05221	A Model to Search for Synthesizable Molecules	Deep generative models are able to suggest new organic molecules by generating strings, trees, and graphs representing their structure. While such models allow one to generate molecules with desirable properties, they give no guarantees that the molecules can actually be synthesized in practice. We propose a new molecu...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	134,962
2307.09311	Automatic Differentiation for Inverse Problems with Applications in Quantum Transport	A neural solver and differentiable simulation of the quantum transmitting boundary model is presented for the inverse quantum transport problem. The neural solver is used to engineer continuous transmission properties and the differentiable simulation is used to engineer current-voltage characteristics.	false	true	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	380,129
1108.4216	Coordination of passive systems under quantized measurements	In this paper we investigate a passivity approach to collective coordination and synchronization problems in the presence of quantized measurements and show that coordination tasks can be achieved in a practical sense for a large class of passive systems.	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	11,755
2301.00975	Surveillance Face Anti-spoofing	Face Anti-spoofing (FAS) is essential to secure face recognition systems from various physical attacks. However, recent research generally focuses on short-distance applications (i.e., phone unlocking) while lacking consideration of long-distance scenes (i.e., surveillance security checks). In order to promote relevant...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	339,092
1704.02935	A Cooperative Enterprise Agent Based Control Architecture	The paper proposes a hierarchical, agent-based, DES supported, distributed architecture for networked organization control. Taking into account enterprise integration engineering frameworks and business process management techniques, the paper intends to apply control engineering approaches for solving some problems of...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	71,540
2206.03441	Robust Sparse Mean Estimation via Sum of Squares	We study the problem of high-dimensional sparse mean estimation in the presence of an $\epsilon$-fraction of adversarial outliers. Prior work obtained sample and computationally efficient algorithms for this task for identity-covariance subgaussian distributions. In this work, we develop the first efficient algorithms ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	301,286
1612.02161	Measuring the non-asymptotic convergence of sequential Monte Carlo samplers using probabilistic programming	A key limitation of sampling algorithms for approximate inference is that it is difficult to quantify their approximation error. Widely used sampling schemes, such as sequential importance sampling with resampling and Metropolis-Hastings, produce output samples drawn from a distribution that may be far from the target ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	65,195
1709.10217	The First Evaluation of Chinese Human-Computer Dialogue Technology	In this paper, we introduce the first evaluation of Chinese human-computer dialogue technology. We detail the evaluation scheme, tasks, metrics and how to collect and annotate the data for training, developing and test. The evaluation includes two tasks, namely user intent classification and online testing of task-orie...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	81,748
2305.05392	Sharpness-Aware Minimization Alone can Improve Adversarial Robustness	Sharpness-Aware Minimization (SAM) is an effective method for improving generalization ability by regularizing loss sharpness. In this paper, we explore SAM in the context of adversarial robustness. We find that using only SAM can achieve superior adversarial robustness without sacrificing clean accuracy compared to st...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	363,133
1711.04114	Mobile Sensing of Two-Dimensional Bandlimited Fields on Random Paths	Mobile sensing has been recently proposed for sampling spatial fields, where mobile sensors record the field along various paths for reconstruction. Classical and contemporary sampling typically assumes that the sampling locations are approximately known. This work explores multiple sampling strategies along random pat...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	84,335
2110.08743	GNN-LM: Language Modeling based on Global Contexts via GNN	Inspired by the notion that ``{\it to copy is easier than to memorize}``, in this work, we introduce GNN-LM, which extends the vanilla neural language model (LM) by allowing to reference similar contexts in the entire training corpus. We build a directed heterogeneous graph between an input context and its semantically...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	261,538
1806.08115	Modeling Word Emotion in Historical Language: Quantity Beats Supposed Stability in Seed Word Selection	To understand historical texts, we must be aware that language -- including the emotional connotation attached to words -- changes over time. In this paper, we aim at estimating the emotion which is associated with a given word in former language stages of English and German. Emotion is represented following the popula...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	101,096
2312.12479	Zero-shot Building Attribute Extraction from Large-Scale Vision and Language Models	Existing building recognition methods, exemplified by BRAILS, utilize supervised learning to extract information from satellite and street-view images for classification and segmentation. However, each task module requires human-annotated data, hindering the scalability and robustness to regional variations and annotat...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	416,981
2204.01571	Coarse-to-Fine Q-attention with Learned Path Ranking	We propose Learned Path Ranking (LPR), a method that accepts an end-effector goal pose, and learns to rank a set of goal-reaching paths generated from an array of path generating methods, including: path planning, Bezier curve sampling, and a learned policy. The core idea being that each of the path generation modules ...	false	false	false	false	true	false	true	true	false	false	false	true	false	false	false	false	false	false	289,647
1702.06408	A Discriminative Event Based Model for Alzheimer's Disease Progression Modeling	The event-based model (EBM) for data-driven disease progression modeling estimates the sequence in which biomarkers for a disease become abnormal. This helps in understanding the dynamics of disease progression and facilitates early diagnosis by staging patients on a disease progression timeline. Existing EBM methods a...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	68,607
2406.11021	$\alpha$-OCC: Uncertainty-Aware Camera-based 3D Semantic Occupancy Prediction	In the realm of autonomous vehicle perception, comprehending 3D scenes is paramount for tasks such as planning and mapping. Camera-based 3D Semantic Occupancy Prediction (OCC) aims to infer scene geometry and semantics from limited observations. While it has gained popularity due to affordability and rich visual cues, ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	464,674
1404.4443	Enhanced List-Based Group-Wise Overloaded Receiver with Application to Satellite Reception	The market trends towards the use of smaller dish antennas for TV satellite receivers, as well as the growing density of broadcasting satellites in orbit require the application of robust adjacent satellite interference (ASI) cancellation algorithms at the receivers. The wider beamwidth of a small size dish and the gro...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	32,400
2201.04807	Active Learning-Based Multistage Sequential Decision-Making Model with Application on Common Bile Duct Stone Evaluation	Multistage sequential decision-making scenarios are commonly seen in the healthcare diagnosis process. In this paper, an active learning-based method is developed to actively collect only the necessary patient data in a sequential manner. There are two novelties in the proposed method. First, unlike the existing ordina...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	275,201
1301.1701	Secrecy Capacity of Two-Hop Relay Assisted Wiretap Channels	Incorporating the physical layer characteristics to secure communications has received considerable attention in recent years. Moreover, cooperation with some nodes of network can give benefits of multiple-antenna systems, increasing the secrecy capacity of such channels. In this paper, we consider cooperative wiretap ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	20,875
2103.00550	A Survey on Deep Semi-supervised Learning	Deep semi-supervised learning is a fast-growing field with a range of practical applications. This paper provides a comprehensive survey on both fundamentals and recent advances in deep semi-supervised learning methods from perspectives of model design and unsupervised loss functions. We first present a taxonomy for de...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	222,322
2103.02174	Dynamic Offloading Loading Optimization in distributed Fault Diagnosis system with Deep Reinforcement Learning Approach	Artificial intelligence and distributed algorithms have been widely used in mechanical fault diagnosis with the explosive growth of diagnostic data. A novel intelligent fault diagnosis system framework that allows intelligent terminals to offload computational tasks to Mobile edge computing (MEC) servers is provided in...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	222,873
2303.09658	Energy Management of Multi-mode Plug-in Hybrid Electric Vehicle using Multi-agent Deep Reinforcement Learning	The recently emerging multi-mode plug-in hybrid electric vehicle (PHEV) technology is one of the pathways making contributions to decarbonization, and its energy management requires multiple-input and multipleoutput (MIMO) control. At the present, the existing methods usually decouple the MIMO control into singleoutput...	false	false	false	false	false	false	true	true	false	false	true	false	false	false	false	false	false	false	352,131
2010.12967	Automated triage of COVID-19 from various lung abnormalities using chest CT features	The outbreak of COVID-19 has lead to a global effort to decelerate the pandemic spread. For this purpose chest computed-tomography (CT) based screening and diagnosis of COVID-19 suspected patients is utilized, either as a support or replacement to reverse transcription-polymerase chain reaction (RT-PCR) test. In this p...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	202,945
2310.00108	Practical Membership Inference Attacks Against Large-Scale Multi-Modal Models: A Pilot Study	Membership inference attacks (MIAs) aim to infer whether a data point has been used to train a machine learning model. These attacks can be employed to identify potential privacy vulnerabilities and detect unauthorized use of personal data. While MIAs have been traditionally studied for simple classification models, re...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	395,822
2408.08143	Unlearnable Examples Detection via Iterative Filtering	Deep neural networks are proven to be vulnerable to data poisoning attacks. Recently, a specific type of data poisoning attack known as availability attacks has led to the failure of data utilization for model learning by adding imperceptible perturbations to images. Consequently, it is quite beneficial and challenging...	false	false	false	false	false	false	false	false	false	false	false	true	true	false	false	false	false	false	480,875
2112.00582	Transformer-based Network for RGB-D Saliency Detection	RGB-D saliency detection integrates information from both RGB images and depth maps to improve prediction of salient regions under challenging conditions. The key to RGB-D saliency detection is to fully mine and fuse information at multiple scales across the two modalities. Previous approaches tend to apply the multi-s...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	269,182
2105.03458	Duplex Sequence-to-Sequence Learning for Reversible Machine Translation	Sequence-to-sequence learning naturally has two directions. How to effectively utilize supervision signals from both directions? Existing approaches either require two separate models, or a multitask-learned model but with inferior performance. In this paper, we propose REDER (Reversible Duplex Transformer), a paramete...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	234,152
0704.0217	Capacity of a Multiple-Antenna Fading Channel with a Quantized Precoding Matrix	Given a multiple-input multiple-output (MIMO) channel, feedback from the receiver can be used to specify a transmit precoding matrix, which selectively activates the strongest channel modes. Here we analyze the performance of Random Vector Quantization (RVQ), in which the precoding matrix is selected from a random code...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	5
1910.10892	Fast and Differentiable Message Passing on Pairwise Markov Random Fields	Despite the availability of many Markov Random Field (MRF) optimization algorithms, their widespread usage is currently limited due to imperfect MRF modelling arising from hand-crafted model parameters and the selection of inferior inference algorithm. In addition to differentiability, the two main aspects that enable ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	150,619
2410.08794	M$^3$-Impute: Mask-guided Representation Learning for Missing Value Imputation	Missing values are a common problem that poses significant challenges to data analysis and machine learning. This problem necessitates the development of an effective imputation method to fill in the missing values accurately, thereby enhancing the overall quality and utility of the datasets. Existing imputation method...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	497,275
2006.04418	Learning Long-Term Dependencies in Irregularly-Sampled Time Series	Recurrent neural networks (RNNs) with continuous-time hidden states are a natural fit for modeling irregularly-sampled time series. These models, however, face difficulties when the input data possess long-term dependencies. We prove that similar to standard RNNs, the underlying reason for this issue is the vanishing o...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	180,685
2211.09925	FairMILE: Towards an Efficient Framework for Fair Graph Representation Learning	Graph representation learning models have demonstrated great capability in many real-world applications. Nevertheless, prior research indicates that these models can learn biased representations leading to discriminatory outcomes. A few works have been proposed to mitigate the bias in graph representations. However, mo...	false	false	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	331,145
2409.14583	Evaluating Gender, Racial, and Age Biases in Large Language Models: A Comparative Analysis of Occupational and Crime Scenarios	Recent advancements in Large Language Models(LLMs) have been notable, yet widespread enterprise adoption remains limited due to various constraints. This paper examines bias in LLMs-a crucial issue affecting their usability, reliability, and fairness. Researchers are developing strategies to mitigate bias, including de...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	490,533
2108.10703	REFINE: Random RangE FInder for Network Embedding	Network embedding approaches have recently attracted considerable interest as they learn low-dimensional vector representations of nodes. Embeddings based on the matrix factorization are effective but they are usually computationally expensive due to the eigen-decomposition step. In this paper, we propose a Random Rang...	false	false	false	true	true	false	true	false	false	false	false	false	false	false	false	false	false	false	251,981
1806.10359	Context Proposals for Saliency Detection	One of the fundamental properties of a salient object region is its contrast with the immediate context. The problem is that numerous object regions exist which potentially can all be salient. One way to prevent an exhaustive search over all object regions is by using object proposal algorithms. These return a limited ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	101,536
1904.06031	EvalNorm: Estimating Batch Normalization Statistics for Evaluation	Batch normalization (BN) has been very effective for deep learning and is widely used. However, when training with small minibatches, models using BN exhibit a significant degradation in performance. In this paper we study this peculiar behavior of BN to gain a better understanding of the problem, and identify a cause....	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	127,463
2403.08917	Efficiently Computing Similarities to Private Datasets	Many methods in differentially private model training rely on computing the similarity between a query point (such as public or synthetic data) and private data. We abstract out this common subroutine and study the following fundamental algorithmic problem: Given a similarity function $f$ and a large high-dimensional p...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	true	437,538
2108.04355	Hyperparameter Analysis for Derivative Compressive Sampling	Derivative compressive sampling (DCS) is a signal reconstruction method from measurements of the spatial gradient with sub-Nyquist sampling rate. Applications of DCS include optical image reconstruction, photometric stereo, and shape-from-shading. In this work, we study the sensitivity of DCS with respect to algorithmi...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	249,975
2112.06672	Tree-Based Dynamic Classifier Chains	Classifier chains are an effective technique for modeling label dependencies in multi-label classification. However, the method requires a fixed, static order of the labels. While in theory, any order is sufficient, in practice, this order has a substantial impact on the quality of the final prediction. Dynamic classif...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	271,261
1210.0866	Classification of Hepatic Lesions using the Matching Metric	In this paper we present a methodology of classifying hepatic (liver) lesions using multidimensional persistent homology, the matching metric (also called the bottleneck distance), and a support vector machine. We present our classification results on a dataset of 132 lesions that have been outlined and annotated by ra...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	18,909
2111.09451	Benchmarking and scaling of deep learning models for land cover image classification	The availability of the sheer volume of Copernicus Sentinel-2 imagery has created new opportunities for exploiting deep learning (DL) methods for land use land cover (LULC) image classification. However, an extensive set of benchmark experiments is currently lacking, i.e. DL models tested on the same dataset, with a co...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	267,014
1810.06065	Robust Neural Abstractive Summarization Systems and Evaluation against Adversarial Information	Sequence-to-sequence (seq2seq) neural models have been actively investigated for abstractive summarization. Nevertheless, existing neural abstractive systems frequently generate factually incorrect summaries and are vulnerable to adversarial information, suggesting a crucial lack of semantic understanding. In this pape...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	110,373
2406.15430	Automated Parking Planning with Vision-Based BEV Approach	Automated Valet Parking (AVP) is a crucial component of advanced autonomous driving systems, focusing on the endpoint task within the "human-vehicle interaction" process to tackle the challenges of the "last mile".The perception module of the automated parking algorithm has evolved from local perception using ultrasoni...	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	466,727
2101.00536	Computing Cliques and Cavities in Networks	Complex networks contain complete subgraphs such as nodes, edges, triangles, etc., referred to as simplices and cliques of different orders. Notably, cavities consisting of higher-order cliques play an important role in brain functions. Since searching for maximum cliques is an NP-complete problem, we use k-core decomp...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	214,117
2204.01450	Learning Commonsense-aware Moment-Text Alignment for Fast Video Temporal Grounding	Grounding temporal video segments described in natural language queries effectively and efficiently is a crucial capability needed in vision-and-language fields. In this paper, we deal with the fast video temporal grounding (FVTG) task, aiming at localizing the target segment with high speed and favorable accuracy. Mos...	false	false	false	false	false	false	true	false	true	false	false	true	false	false	false	false	false	false	289,604
2304.01201	Neural Volumetric Memory for Visual Locomotion Control	Legged robots have the potential to expand the reach of autonomy beyond paved roads. In this work, we consider the difficult problem of locomotion on challenging terrains using a single forward-facing depth camera. Due to the partial observability of the problem, the robot has to rely on past observations to infer the ...	false	false	false	false	true	false	false	true	false	false	false	true	false	false	false	false	false	false	355,970
2202.11094	GroupViT: Semantic Segmentation Emerges from Text Supervision	Grouping and recognition are important components of visual scene understanding, e.g., for object detection and semantic segmentation. With end-to-end deep learning systems, grouping of image regions usually happens implicitly via top-down supervision from pixel-level recognition labels. Instead, in this paper, we prop...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	281,767
1703.08577	Balancing Selection Pressures, Multiple Objectives, and Neural Modularity to Coevolve Cooperative Agent Behavior	Previous research using evolutionary computation in Multi-Agent Systems indicates that assigning fitness based on team vs.\ individual behavior has a strong impact on the ability of evolved teams of artificial agents to exhibit teamwork in challenging tasks. However, such research only made use of single-objective evol...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	70,605
1310.1840	Parallel coordinate descent for the Adaboost problem	We design a randomised parallel version of Adaboost based on previous studies on parallel coordinate descent. The algorithm uses the fact that the logarithm of the exponential loss is a function with coordinate-wise Lipschitz continuous gradient, in order to define the step lengths. We provide the proof of convergence ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	27,603
1801.09589	Coactivated Clique Based Multisource Overlapping Brain Subnetwork Extraction	Subnetwork extraction using community detection methods is commonly used to study the brain's modular structure. Recent studies indicated that certain brain regions are known to interact with multiple subnetworks. However, most existing methods are mainly for non-overlapping subnetwork extraction. In this paper, we pre...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	89,142
2212.09361	Stochastic stability analysis of legged locomotion using unscented transformation	In this manuscript, we present a novel method for estimating the stochastic stability characteristics of metastable legged systems using the unscented transformation. Prior methods for stability analysis in such systems often required high-dimensional state space discretization and a broad set of initial conditions, re...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	337,088
2407.17515	Quality Diversity for Robot Learning: Limitations and Future Directions	Quality Diversity (QD) has shown great success in discovering high-performing, diverse policies for robot skill learning. While current benchmarks have led to the development of powerful QD methods, we argue that new paradigms must be developed to facilitate open-ended search and generalizability. In particular, many m...	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	476,015
2012.12899	Learning by Self-Explanation, with Application to Neural Architecture Search	Learning by self-explanation is an effective learning technique in human learning, where students explain a learned topic to themselves for deepening their understanding of this topic. It is interesting to investigate whether this explanation-driven learning methodology broadly used by humans is helpful for improving m...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	213,057
cs/0612109	Truncating the loop series expansion for Belief Propagation	Recently, M. Chertkov and V.Y. Chernyak derived an exact expression for the partition sum (normalization constant) corresponding to a graphical model, which is an expansion around the Belief Propagation solution. By adding correction terms to the BP free energy, one for each "generalized loop" in the factor graph, the ...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	539,983
2105.12018	Towards a method to anticipate dark matter signals with deep learning at the LHC	We study several simplified dark matter (DM) models and their signatures at the LHC using neural networks. We focus on the usual monojet plus missing transverse energy channel, but to train the algorithms we organize the data in 2D histograms instead of event-by-event arrays. This results in a large performance boost t...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	236,887
2001.04780	Age-of-Information Dependent Random Access for Massive IoT Networks	As the most well-known application of the Internet of Things (IoT), remote monitoring is now pervasive. In these monitoring applications, information usually has a higher value when it is fresher. A new metric, termed the age of information (AoI), has recently been proposed to quantify the information freshness in vari...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	160,353
2012.02312	ReMix: Calibrated Resampling for Class Imbalance in Deep learning	Class imbalance is a problem of significant importance in applied deep learning where trained models are exploited for decision support and automated decisions in critical areas such as health and medicine, transportation, and finance. The challenge of learning deep models from imbalanced training data remains high, an...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	209,727
1405.3224	On the Complexity of A/B Testing	A/B testing refers to the task of determining the best option among two alternatives that yield random outcomes. We provide distribution-dependent lower bounds for the performance of A/B testing that improve over the results currently available both in the fixed-confidence (or delta-PAC) and fixed-budget settings. When...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	33,064
1005.1785	Sidelobe Suppression for Robust Beamformer via The Mixed Norm Constraint	Applying a sparse constraint on the beam pattern has been suggested to suppress the sidelobe of the minimum variance distortionless response (MVDR) beamformer recently. To further improve the performance, we add a mixed norm constraint on the beam pattern. It matches the beam pattern better and encourages dense distrib...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	6,457
2301.10859	Salesforce CausalAI Library: A Fast and Scalable Framework for Causal Analysis of Time Series and Tabular Data	We introduce the Salesforce CausalAI Library, an open-source library for causal analysis using observational data. It supports causal discovery and causal inference for tabular and time series data, of discrete, continuous and heterogeneous types. This library includes algorithms that handle linear and non-linear causa...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	341,925
2403.04164	ProMISe: Promptable Medical Image Segmentation using SAM	With the proposal of the Segment Anything Model (SAM), fine-tuning SAM for medical image segmentation (MIS) has become popular. However, due to the large size of the SAM model and the significant domain gap between natural and medical images, fine-tuning-based strategies are costly with potential risk of instability, f...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	435,485
1111.0432	Approximate Stochastic Subgradient Estimation Training for Support Vector Machines	Subgradient algorithms for training support vector machines have been quite successful for solving large-scale and online learning problems. However, they have been restricted to linear kernels and strongly convex formulations. This paper describes efficient subgradient approaches without such limitations. Our approach...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	12,875
2106.12735	Multi-Modal 3D Object Detection in Autonomous Driving: a Survey	In this survey, we first introduce the background of popular sensors used for self-driving, their data properties, and the corresponding object detection algorithms. Next, we discuss existing datasets that can be used for evaluating multi-modal 3D object detection algorithms. Then we present a review of multi-modal fus...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	242,821
2106.15412	An Efficient Batch Constrained Bayesian Optimization Approach for Analog Circuit Synthesis via Multi-objective Acquisition Ensemble	Bayesian optimization is a promising methodology for analog circuit synthesis. However, the sequential nature of the Bayesian optimization framework significantly limits its ability to fully utilize real-world computational resources. In this paper, we propose an efficient parallelizable Bayesian optimization algorithm...	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	243,761
2305.19229	FedDisco: Federated Learning with Discrepancy-Aware Collaboration	This work considers the category distribution heterogeneity in federated learning. This issue is due to biased labeling preferences at multiple clients and is a typical setting of data heterogeneity. To alleviate this issue, most previous works consider either regularizing local models or fine-tuning the global model, ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	369,432
2312.06516	Irregular Repetition Slotted Aloha with Multipacket Detection: A Density Evolution Analysis	Irregular repetition slotted Aloha (IRSA) has shown significant advantages as a modern technique for uncoordinated random access with massive number of users due to its capability of achieving theoretically a throughput of $1$ packet per slot. When the receiver has also the multi-packet reception of multi-user (MUD) de...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	414,546
2411.14574	SRSA: A Cost-Efficient Strategy-Router Search Agent for Real-world Human-Machine Interactions	Recently, as Large Language Models (LLMs) have shown impressive emerging capabilities and gained widespread popularity, research on LLM-based search agents has proliferated. In real-world situations, users often input contextual and highly personalized queries to chatbots, challenging LLMs to capture context and genera...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	510,237
2006.16993	Feature Extraction for Novelty Detection in Network Traffic	Data representation plays a critical role in the performance of novelty detection (or ``anomaly detection'') methods in machine learning. The data representation of network traffic often determines the effectiveness of these models as much as the model itself. The wide range of novel events that network operators need ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	184,972
2104.01836	Stopping Criterion for Active Learning Based on Error Stability	Active learning is a framework for supervised learning to improve the predictive performance by adaptively annotating a small number of samples. To realize efficient active learning, both an acquisition function that determines the next datum and a stopping criterion that determines when to stop learning should be cons...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	228,497
2201.06889	Boosting Robustness of Image Matting with Context Assembling and Strong Data Augmentation	Deep image matting methods have achieved increasingly better results on benchmarks (e.g., Composition-1k/alphamatting.com). However, the robustness, including robustness to trimaps and generalization to images from different domains, is still under-explored. Although some works propose to either refine the trimaps or a...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	275,871
1303.2826	Probabilistic Topic and Syntax Modeling with Part-of-Speech LDA	This article presents a probabilistic generative model for text based on semantic topics and syntactic classes called Part-of-Speech LDA (POSLDA). POSLDA simultaneously uncovers short-range syntactic patterns (syntax) and long-range semantic patterns (topics) that exist in document collections. This results in word dis...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	22,873
2105.12917	BSNN: Towards Faster and Better Conversion of Artificial Neural Networks to Spiking Neural Networks with Bistable Neurons	The spiking neural network (SNN) computes and communicates information through discrete binary events. It is considered more biologically plausible and more energy-efficient than artificial neural networks (ANN) in emerging neuromorphic hardware. However, due to the discontinuous and non-differentiable characteristics,...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	false	false	237,145
2501.18535	A Hybrid Data-Driven Approach For Analyzing And Predicting Inpatient Length Of Stay In Health Centre	Patient length of stay (LoS) is a critical metric for evaluating the efficacy of hospital management. The primary objectives encompass to improve efficiency and reduce costs while enhancing patient outcomes and hospital capacity within the patient journey. By seamlessly merging data-driven techniques with simulation me...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	528,742
0904.1538	Shannon-Kotel'nikov Mappings for Analog Point-to-Point Communications	In this paper an approach to joint source-channel coding (JSCC) named Shannon-Kotel'nikov mappings (S-K mappings) is presented. S-K mappings are continuous, or piecewise continuous direct source-to-channel mappings operating directly on amplitude continuous and discrete time signals. Such mappings include several exist...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	3,515

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