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
2305.05772	Spiking Neural Networks in the Alexiewicz Topology: A New Perspective on Analysis and Error Bounds	In order to ease the analysis of error propagation in neuromorphic computing and to get a better understanding of spiking neural networks (SNN), we address the problem of mathematical analysis of SNNs as endomorphisms that map spike trains to spike trains. A central question is the adequate structure for a space of spi...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	true	363,271
2402.13987	A Simple and Yet Fairly Effective Defense for Graph Neural Networks	Graph Neural Networks (GNNs) have emerged as the dominant approach for machine learning on graph-structured data. However, concerns have arisen regarding the vulnerability of GNNs to small adversarial perturbations. Existing defense methods against such perturbations suffer from high time complexity and can negatively ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	431,486
2007.08607	Optimization of Surface Plasmon Resonance Biosensor for Analysis of Lipid Molecules	Surface Plasmon Resonance (SPR) is an important bio-sensing technique for real-time label-free detection. However, it is pivotal to optimize various parameters of the sensor configuration for efficient and highly sensitive sensing. To that effect, we focus on optimizing two different SPR structures -- the basic Kretsch...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	187,679
2012.06060	Spatially Conditioned Graphs for Detecting Human-Object Interactions	We address the problem of detecting human-object interactions in images using graphical neural networks. Unlike conventional methods, where nodes send scaled but otherwise identical messages to each of their neighbours, we propose to condition messages between pairs of nodes on their spatial relationships, resulting in...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	210,982
2407.21571	PMoE: Progressive Mixture of Experts with Asymmetric Transformer for Continual Learning	Large Language Models (LLMs) encounter significant challenges in continual learning due to catastrophic forgetting, where new information overwrites previously acquired knowledge. This limitation leads to substantial environmental and economic waste. In this study, we introduce the PMoE, Progressive Mixture of Experts ...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	477,595
1905.06618	On the Fairness of Time-Critical Influence Maximization in Social Networks	Influence maximization has found applications in a wide range of real-world problems, for instance, viral marketing of products in an online social network, and information propagation of valuable information such as job vacancy advertisements and health-related information. While existing algorithmic techniques usuall...	false	false	false	true	false	false	false	false	false	false	false	false	false	true	false	false	false	false	131,042
2501.10062	OMoE: Diversifying Mixture of Low-Rank Adaptation by Orthogonal Finetuning	Building mixture-of-experts (MoE) architecture for Low-rank adaptation (LoRA) is emerging as a potential direction in parameter-efficient fine-tuning (PEFT) for its modular design and remarkable performance. However, simply stacking the number of experts cannot guarantee significant improvement. In this work, we first ...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	525,370
2003.13419	Use of fitted polynomials for the decentralized estimation of network variables in unbalanced radial LV feeders	The lack of comprehensive monitoring equipment in low voltage (LV) residential feeders, impedes a near-term deployment of centralized schemes for the integration of domestic-scale distributed generation (DG). In this context, this paper introduces a technique that generates a set of fitted polynomials, derived from off...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	170,202
1906.08230	Evaluating Protein Transfer Learning with TAPE	Protein modeling is an increasingly popular area of machine learning research. Semi-supervised learning has emerged as an important paradigm in protein modeling due to the high cost of acquiring supervised protein labels, but the current literature is fragmented when it comes to datasets and standardized evaluation tec...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	135,810
1908.00328	ScarfNet: Multi-scale Features with Deeply Fused and Redistributed Semantics for Enhanced Object Detection	Convolutional neural network (CNN) has led to significant progress in object detection. In order to detect the objects in various sizes, the object detectors often exploit the hierarchy of the multi-scale feature maps called feature pyramid, which is readily obtained by the CNN architecture. However, the performance of...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	140,489
0812.0885	Elementary epistemological features of machine intelligence	Theoretical analysis of machine intelligence (MI) is useful for defining a common platform in both theoretical and applied artificial intelligence (AI). The goal of this paper is to set canonical definitions that can assist pragmatic research in both strong and weak AI. Described epistemological features of machine int...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	2,744
2306.16354	cuSLINK: Single-linkage Agglomerative Clustering on the GPU	In this paper, we propose cuSLINK, a novel and state-of-the-art reformulation of the SLINK algorithm on the GPU which requires only $O(Nk)$ space and uses a parameter $k$ to trade off space and time. We also propose a set of novel and reusable building blocks that compose cuSLINK. These building blocks include highly o...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	376,346
2205.04060	Approaches to the classification of complex systems: Words, texts, and more	The Chapter starts with introductory information about quantitative linguistics notions, like rank--frequency dependence, Zipf's law, frequency spectra, etc. Similarities in distributions of words in texts with level occupation in quantum ensembles hint at a superficial analogy with statistical physics. This enables on...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	295,521
1311.2795	Complete solution of a constrained tropical optimization problem with application to location analysis	We present a multidimensional optimization problem that is formulated and solved in the tropical mathematics setting. The problem consists of minimizing a nonlinear objective function defined on vectors over an idempotent semifield by means of a conjugate transposition operator, subject to constraints in the form of li...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	28,351
1704.04760	In-Datacenter Performance Analysis of a Tensor Processing Unit	Many architects believe that major improvements in cost-energy-performance must now come from domain-specific hardware. This paper evaluates a custom ASIC---called a Tensor Processing Unit (TPU)---deployed in datacenters since 2015 that accelerates the inference phase of neural networks (NN). The heart of the TPU is a ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	true	71,883
2406.14799	Capture Point Control in Thruster-Assisted Bipedal Locomotion	Despite major advancements in control design that are robust to unplanned disturbances, bipedal robots are still susceptible to falling over and struggle to negotiate rough terrains. By utilizing thrusters in our bipedal robot, we can perform additional posture manipulation and expand the modes of locomotion to enhance...	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	466,474
2104.05379	Comparing the Benefit of Synthetic Training Data for Various Automatic Speech Recognition Architectures	Recent publications on automatic-speech-recognition (ASR) have a strong focus on attention encoder-decoder (AED) architectures which tend to suffer from over-fitting in low resource scenarios. One solution to tackle this issue is to generate synthetic data with a trained text-to-speech system (TTS) if additional text i...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	229,706
2007.09919	Robust Tracking against Adversarial Attacks	While deep convolutional neural networks (CNNs) are vulnerable to adversarial attacks, considerably few efforts have been paid to construct robust deep tracking algorithms against adversarial attacks. Current studies on adversarial attack and defense mainly reside in a single image. In this work, we first attempt to ge...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	188,111
2309.00649	GPT has become financially literate: Insights from financial literacy tests of GPT and a preliminary test of how people use it as a source of advice	We assess the ability of GPT -- a large language model -- to serve as a financial robo-advisor for the masses, by using a financial literacy test. Davinci and ChatGPT based on GPT-3.5 score 66% and 65% on the financial literacy test, respectively, compared to a baseline of 33%. However, ChatGPT based on GPT-4 achieves ...	false	false	false	false	true	false	false	false	true	false	false	false	false	true	false	false	false	false	389,376
2401.14426	M$^3$TN: Multi-gate Mixture-of-Experts based Multi-valued Treatment Network for Uplift Modeling	Uplift modeling is a technique used to predict the effect of a treatment (e.g., discounts) on an individual's response. Although several methods have been proposed for multi-valued treatment, they are extended from binary treatment methods. There are still some limitations. Firstly, existing methods calculate uplift ba...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	424,087
2006.08643	On the training dynamics of deep networks with $L_2$ regularization	We study the role of $L_2$ regularization in deep learning, and uncover simple relations between the performance of the model, the $L_2$ coefficient, the learning rate, and the number of training steps. These empirical relations hold when the network is overparameterized. They can be used to predict the optimal regular...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	182,247
2103.17007	Tossing Quantum Coins and Dice	The procedure of tossing quantum coins and dice is described. This case is an important example of a quantum procedure because it presents a typical framework employed in quantum information processing and quantum computing. The emphasis is on the clarification of the difference between quantum and classical conditiona...	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	false	false	227,762
2202.06851	HAKE: A Knowledge Engine Foundation for Human Activity Understanding	Human activity understanding is of widespread interest in artificial intelligence and spans diverse applications like health care and behavior analysis. Although there have been advances in deep learning, it remains challenging. The object recognition-like solutions usually try to map pixels to semantics directly, but ...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	280,354
2202.02171	NeAT: Neural Adaptive Tomography	In this paper, we present Neural Adaptive Tomography (NeAT), the first adaptive, hierarchical neural rendering pipeline for multi-view inverse rendering. Through a combination of neural features with an adaptive explicit representation, we achieve reconstruction times far superior to existing neural inverse rendering m...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	278,719
1003.5893	Recognition of Handwritten Textual Annotations using Tesseract Open Source OCR Engine for information Just In Time (iJIT)	Objective of the current work is to develop an Optical Character Recognition (OCR) engine for information Just In Time (iJIT) system that can be used for recognition of handwritten textual annotations of lower case Roman script. Tesseract open source OCR engine under Apache License 2.0 is used to develop user-specific ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	6,038
1902.04696	Using Approximate Models in Robot Learning	Trajectory following is one of the complicated control problems when its dynamics are nonlinear, stochastic and include a large number of parameters. The problem has significant difficulties including a large number of trials required for data collection and a massive volume of computations required to find a closed-lo...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	121,397
2012.07619	What Makes a Good and Useful Summary? Incorporating Users in Automatic Summarization Research	Automatic text summarization has enjoyed great progress over the years and is used in numerous applications, impacting the lives of many. Despite this development, there is little research that meaningfully investigates how the current research focus in automatic summarization aligns with users' needs. To bridge this g...	true	false	false	false	true	true	false	false	true	false	false	false	false	false	false	false	false	false	211,525
2201.05917	Skydiving Technique Analysis from a Control Engineering Perspective: Developing a Tool for Aiding Motor Learning	This study offers an interdisciplinary approach to movement technique analysis, designed to deal with intensive interaction between an environment and a trainee. The free-fall stage of skydiving is investigated, when aerial maneuvers are performed by changing the body posture and thus deflecting the surrounding airflow...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	275,546
2007.07606	timeXplain -- A Framework for Explaining the Predictions of Time Series Classifiers	Modern time series classifiers display impressive predictive capabilities, yet their decision-making processes mostly remain black boxes to the user. At the same time, model-agnostic explainers, such as the recently proposed SHAP, promise to make the predictions of machine learning models interpretable, provided there ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	187,384
2302.06492	Optical flow estimation from event-based cameras and spiking neural networks	Event-based cameras are raising interest within the computer vision community. These sensors operate with asynchronous pixels, emitting events, or "spikes", when the luminance change at a given pixel since the last event surpasses a certain threshold. Thanks to their inherent qualities, such as their low power consumpt...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	345,423
2302.00441	Scaling Laws for Hyperparameter Optimization	Hyperparameter optimization is an important subfield of machine learning that focuses on tuning the hyperparameters of a chosen algorithm to achieve peak performance. Recently, there has been a stream of methods that tackle the issue of hyperparameter optimization, however, most of the methods do not exploit the domina...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	343,217
2208.02938	Abstract Interpretation for Generalized Heuristic Search in Model-Based Planning	Domain-general model-based planners often derive their generality by constructing search heuristics through the relaxation or abstraction of symbolic world models. We illustrate how abstract interpretation can serve as a unifying framework for these abstraction-based heuristics, extending the reach of heuristic search ...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	311,621
2006.10129	Smoothed Analysis of Online and Differentially Private Learning	Practical and pervasive needs for robustness and privacy in algorithms have inspired the design of online adversarial and differentially private learning algorithms. The primary quantity that characterizes learnability in these settings is the Littlestone dimension of the class of hypotheses [Ben-David et al., 2009, Al...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	182,772
1806.02193	GraKeL: A Graph Kernel Library in Python	The problem of accurately measuring the similarity between graphs is at the core of many applications in a variety of disciplines. Graph kernels have recently emerged as a promising approach to this problem. There are now many kernels, each focusing on different structural aspects of graphs. Here, we present GraKeL, a ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	99,723
2103.01737	Distilling Causal Effect of Data in Class-Incremental Learning	We propose a causal framework to explain the catastrophic forgetting in Class-Incremental Learning (CIL) and then derive a novel distillation method that is orthogonal to the existing anti-forgetting techniques, such as data replay and feature/label distillation. We first 1) place CIL into the framework, 2) answer why ...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	222,729
2106.16118	SimNet: Enabling Robust Unknown Object Manipulation from Pure Synthetic Data via Stereo	Robot manipulation of unknown objects in unstructured environments is a challenging problem due to the variety of shapes, materials, arrangements and lighting conditions. Even with large-scale real-world data collection, robust perception and manipulation of transparent and reflective objects across various lighting co...	false	false	false	false	false	false	true	true	false	false	false	true	false	false	false	false	false	false	243,978
2106.06027	Sparse and Imperceptible Adversarial Attack via a Homotopy Algorithm	Sparse adversarial attacks can fool deep neural networks (DNNs) by only perturbing a few pixels (regularized by l_0 norm). Recent efforts combine it with another l_infty imperceptible on the perturbation magnitudes. The resultant sparse and imperceptible attacks are practically relevant, and indicate an even higher vul...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	240,320
2409.09063	TS-EoH: An Edge Server Task Scheduling Algorithm Based on Evolution of Heuristic	With the widespread adoption of 5G and Internet of Things (IoT) technologies, the low latency provided by edge computing has great importance for real-time processing. However, managing numerous simultaneous service requests poses a significant challenge to maintaining low latency. Current edge server task scheduling m...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	488,160
1809.02888	Lost in the Digital Wild: Hiding Information in Digital Activities	This paper presents a new general framework of information hiding, in which the hidden information is embedded into a collection of activities conducted by selected human and computer entities (e.g., a number of online accounts of one or more online social networks) in a selected digital world. Different from other tra...	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	false	false	false	107,168
2004.01790	Sifter: A Hybrid Workflow for Theme-based Video Curation at Scale	User-generated content platforms curate their vast repositories into thematic compilations that facilitate the discovery of high-quality material. Platforms that seek tight editorial control employ people to do this curation, but this process involves time-consuming routine tasks, such as sifting through thousands of v...	true	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	171,007
2111.08693	Inverting brain grey matter models with likelihood-free inference: a tool for trustable cytoarchitecture measurements	Effective characterisation of the brain grey matter cytoarchitecture with quantitative sensitivity to soma density and volume remains an unsolved challenge in diffusion MRI (dMRI). Solving the problem of relating the dMRI signal with cytoarchitectural characteristics calls for the definition of a mathematical model tha...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	266,781
2410.16089	Multi-Sensor Fusion for UAV Classification Based on Feature Maps of Image and Radar Data	The unique cost, flexibility, speed, and efficiency of modern UAVs make them an attractive choice in many applications in contemporary society. This, however, causes an ever-increasing number of reported malicious or accidental incidents, rendering the need for the development of UAV detection and classification mechan...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	500,866
1212.2498	Learning Continuous Time Bayesian Networks	Continuous time Bayesian networks (CTBNs) describe structured stochastic processes with finitely many states that evolve over continuous time. A CTBN is a directed (possibly cyclic) dependency graph over a set of variables, each of which represents a finite state continuous time Markov process whose transition model is...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	20,301
2403.00944	Optimizing Dynamic Balance in a Rat Robot via the Lateral Flexion of a Soft Actuated Spine	Balancing oneself using the spine is a physiological alignment of the body posture in the most efficient manner by the muscular forces for mammals. For this reason, we can see many disabled quadruped animals can still stand or walk even with three limbs. This paper investigates the optimization of dynamic balance durin...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	434,193
1911.05146	HyPar-Flow: Exploiting MPI and Keras for Scalable Hybrid-Parallel DNN Training using TensorFlow	To reduce training time of large-scale DNNs, scientists have started to explore parallelization strategies like data-parallelism, model-parallelism, and hybrid-parallelism. While data-parallelism has been extensively studied and developed, several problems exist in realizing model-parallelism and hybrid-parallelism eff...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	true	153,174
2304.07883	Bent & Broken Bicycles: Leveraging synthetic data for damaged object re-identification	Instance-level object re-identification is a fundamental computer vision task, with applications from image retrieval to intelligent monitoring and fraud detection. In this work, we propose the novel task of damaged object re-identification, which aims at distinguishing changes in visual appearance due to deformations ...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	true	358,508
1212.0692	An Empirical Evaluation of Portfolios Approaches for solving CSPs	Recent research in areas such as SAT solving and Integer Linear Programming has shown that the performances of a single arbitrarily efficient solver can be significantly outperformed by a portfolio of possibly slower on-average solvers. We report an empirical evaluation and comparison of portfolio approaches applied to...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	20,115
2402.05460	I-FENN with Temporal Convolutional Networks: expediting the load-history analysis of non-local gradient damage propagation	In this paper, we demonstrate for the first time how the Integrated Finite Element Neural Network (I-FENN) framework, previously proposed by the authors, can efficiently simulate the entire loading history of non-local gradient damage propagation. To achieve this goal, we first adopt a Temporal Convolutional Network (T...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	427,872
2310.11758	Domain-Generalized Face Anti-Spoofing with Unknown Attacks	Although face anti-spoofing (FAS) methods have achieved remarkable performance on specific domains or attack types, few studies have focused on the simultaneous presence of domain changes and unknown attacks, which is closer to real application scenarios. To handle domain-generalized unknown attacks, we introduce a new...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	400,771
2406.13257	Reasoning with trees: interpreting CNNs using hierarchies	Challenges persist in providing interpretable explanations for neural network reasoning in explainable AI (xAI). Existing methods like Integrated Gradients produce noisy maps, and LIME, while intuitive, may deviate from the model's reasoning. We introduce a framework that uses hierarchical segmentation techniques for f...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	465,774
2306.04076	Text-only Domain Adaptation using Unified Speech-Text Representation in Transducer	Domain adaptation using text-only corpus is challenging in end-to-end(E2E) speech recognition. Adaptation by synthesizing audio from text through TTS is resource-consuming. We present a method to learn Unified Speech-Text Representation in Conformer Transducer(USTR-CT) to enable fast domain adaptation using the text-on...	false	false	true	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	371,592
2306.02982	PolyVoice: Language Models for Speech to Speech Translation	We propose PolyVoice, a language model-based framework for speech-to-speech translation (S2ST) system. Our framework consists of two language models: a translation language model and a speech synthesis language model. We use discretized speech units, which are generated in a fully unsupervised way, and thus our framewo...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	371,140
2403.09930	Quality-Diversity Actor-Critic: Learning High-Performing and Diverse Behaviors via Value and Successor Features Critics	A key aspect of intelligence is the ability to demonstrate a broad spectrum of behaviors for adapting to unexpected situations. Over the past decade, advancements in deep reinforcement learning have led to groundbreaking achievements to solve complex continuous control tasks. However, most approaches return only one so...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	437,965
2005.00891	Zero-Shot Transfer Learning with Synthesized Data for Multi-Domain Dialogue State Tracking	Zero-shot transfer learning for multi-domain dialogue state tracking can allow us to handle new domains without incurring the high cost of data acquisition. This paper proposes new zero-short transfer learning technique for dialogue state tracking where the in-domain training data are all synthesized from an abstract d...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	175,422
2010.03550	Understanding Clinical Trial Reports: Extracting Medical Entities and Their Relations	The best evidence concerning comparative treatment effectiveness comes from clinical trials, the results of which are reported in unstructured articles. Medical experts must manually extract information from articles to inform decision-making, which is time-consuming and expensive. Here we consider the end-to-end task ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	199,437
2008.00104	Optimizing Long-term Social Welfare in Recommender Systems: A Constrained Matching Approach	Most recommender systems (RS) research assumes that a user's utility can be maximized independently of the utility of the other agents (e.g., other users, content providers). In realistic settings, this is often not true---the dynamics of an RS ecosystem couple the long-term utility of all agents. In this work, we expl...	false	false	false	false	true	true	true	false	false	false	false	false	false	false	false	false	false	false	189,899
2310.02573	Robust Collision Detection for Robots with Variable Stiffness Actuation by Using MAD-CNN: Modularized-Attention-Dilated Convolutional Neural Network	Ensuring safety is paramount in the field of collaborative robotics to mitigate the risks of human injury and environmental damage. Apart from collision avoidance, it is crucial for robots to rapidly detect and respond to unexpected collisions. While several learning-based collision detection methods have been introduc...	true	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	396,911
2301.06527	XNLI 2.0: Improving XNLI dataset and performance on Cross Lingual Understanding (XLU)	Natural Language Processing systems are heavily dependent on the availability of annotated data to train practical models. Primarily, models are trained on English datasets. In recent times, significant advances have been made in multilingual understanding due to the steeply increasing necessity of working in different...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	340,665
2408.15265	Multitask Fine-Tuning and Generative Adversarial Learning for Improved Auxiliary Classification	In this study, we implement a novel BERT architecture for multitask fine-tuning on three downstream tasks: sentiment classification, paraphrase detection, and semantic textual similarity prediction. Our model, Multitask BERT, incorporates layer sharing and a triplet architecture, custom sentence pair tokenization, loss...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	483,871
1906.01127	Proximal Reliability Optimization for Reinforcement Learning	Despite the numerous advances, reinforcement learning remains away from widespread acceptance for autonomous controller design as compared to classical methods due to lack of ability to effectively tackle the reality gap. The reliance on absolute or deterministic reward as a metric for optimization process renders rein...	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	133,602
2411.10669	Awaker2.5-VL: Stably Scaling MLLMs with Parameter-Efficient Mixture of Experts	As the research of Multimodal Large Language Models (MLLMs) becomes popular, an advancing MLLM model is typically required to handle various textual and visual tasks (e.g., VQA, Detection, OCR, and ChartQA) simultaneously for real-world applications. However, due to the significant differences in representation and dis...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	508,738
2112.07219	A real-time spatiotemporal AI model analyzes skill in open surgical videos	Open procedures represent the dominant form of surgery worldwide. Artificial intelligence (AI) has the potential to optimize surgical practice and improve patient outcomes, but efforts have focused primarily on minimally invasive techniques. Our work overcomes existing data limitations for training AI models by curatin...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	271,412
1209.0053	A Session Based Blind Watermarking Technique within the NROI of Retinal Fundus Images for Authentication Using DWT, Spread Spectrum and Harris Corner Detection	Digital Retinal Fundus Images helps to detect various ophthalmic diseases by detecting morphological changes in optical cup, optical disc and macula. Present work proposes a method for the authentication of medical images based on Discrete Wavelet Transformation (DWT) and Spread Spectrum. Proper selection of the Non Re...	false	false	false	false	false	false	false	false	false	false	false	true	false	true	false	false	false	false	18,337
2207.07403	PodcastMix: A dataset for separating music and speech in podcasts	We introduce PodcastMix, a dataset formalizing the task of separating background music and foreground speech in podcasts. We aim at defining a benchmark suitable for training and evaluating (deep learning) source separation models. To that end, we release a large and diverse training dataset based on programatically ge...	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	308,197
1506.07980	A Java Implementation of the SGA, UMDA, ECGA, and HBOA	The Simple Genetic Algorithm, the Univariate Marginal Distribution Algorithm, the Extended Compact Genetic Algorithm, and the Hierarchical Bayesian Optimization Algorithm are all well known Evolutionary Algorithms. In this report we present a Java implementation of these four algorithms with detailed instructions on ...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	true	44,567
2309.15284	A Physics Enhanced Residual Learning (PERL) Framework for Vehicle Trajectory Prediction	In vehicle trajectory prediction, physics models and data-driven models are two predominant methodologies. However, each approach presents its own set of challenges: physics models fall short in predictability, while data-driven models lack interpretability. Addressing these identified shortcomings, this paper proposes...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	394,912
2409.09313	Tensor-Based Synchronization and the Low-Rankness of the Block Trifocal Tensor	The block tensor of trifocal tensors provides crucial geometric information on the three-view geometry of a scene. The underlying synchronization problem seeks to recover camera poses (locations and orientations up to a global transformation) from the block trifocal tensor. We establish an explicit Tucker factorization...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	488,266
2010.12770	Conversational Semantic Parsing for Dialog State Tracking	We consider a new perspective on dialog state tracking (DST), the task of estimating a user's goal through the course of a dialog. By formulating DST as a semantic parsing task over hierarchical representations, we can incorporate semantic compositionality, cross-domain knowledge sharing and co-reference. We present Tr...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	202,839
1506.07924	Decentralized Q-Learning for Stochastic Teams and Games	There are only a few learning algorithms applicable to stochastic dynamic teams and games which generalize Markov decision processes to decentralized stochastic control problems involving possibly self-interested decision makers. Learning in games is generally difficult because of the non-stationary environment in whic...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	44,562
2205.13383	BppAttack: Stealthy and Efficient Trojan Attacks against Deep Neural Networks via Image Quantization and Contrastive Adversarial Learning	Deep neural networks are vulnerable to Trojan attacks. Existing attacks use visible patterns (e.g., a patch or image transformations) as triggers, which are vulnerable to human inspection. In this paper, we propose stealthy and efficient Trojan attacks, BppAttack. Based on existing biology literature on human visual sy...	false	false	false	false	false	false	true	false	false	false	false	true	true	false	false	false	false	false	298,916
2204.02462	Quadratic Approximation Manifold for Mitigating the Kolmogorov Barrier in Nonlinear Projection-Based Model Order Reduction	A quadratic approximation manifold is presented for performing nonlinear, projection-based, model order reduction (PMOR). It constitutes a departure from the traditional affine subspace approximation that is aimed at mitigating the Kolmogorov barrier for nonlinear PMOR, particularly for convection-dominated transport p...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	289,950
2008.00324	Improving Skeleton-based Action Recognitionwith Robust Spatial and Temporal Features	Recently skeleton-based action recognition has made signif-icant progresses in the computer vision community. Most state-of-the-art algorithms are based on Graph Convolutional Networks (GCN), andtarget at improving the network structure of the backbone GCN lay-ers. In this paper, we propose a novel mechanism to learn m...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	189,976
1904.02755	ExCL: Extractive Clip Localization Using Natural Language Descriptions	The task of retrieving clips within videos based on a given natural language query requires cross-modal reasoning over multiple frames. Prior approaches such as sliding window classifiers are inefficient, while text-clip similarity driven ranking-based approaches such as segment proposal networks are far more complicat...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	126,512
2410.12277	A Robot Kinematics Model Estimation Using Inertial Sensors for On-Site Building Robotics	In order to make robots more useful in a variety of environments, they need to be highly portable so that they can be transported to wherever they are needed, and highly storable so that they can be stored when not in use. We propose "on-site robotics", which uses parts procured at the location where the robot will be ...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	498,946
2204.04918	When NAS Meets Trees: An Efficient Algorithm for Neural Architecture Search	The key challenge in neural architecture search (NAS) is designing how to explore wisely in the huge search space. We propose a new NAS method called TNAS (NAS with trees), which improves search efficiency by exploring only a small number of architectures while also achieving a higher search accuracy. TNAS introduces a...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	290,847
2406.05743	Peptide Vaccine Design by Evolutionary Multi-Objective Optimization	Peptide vaccines are growing in significance for fighting diverse diseases. Machine learning has improved the identification of peptides that can trigger immune responses, and the main challenge of peptide vaccine design now lies in selecting an effective subset of peptides due to the allelic diversity among individual...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	462,271
1610.02876	Heuristic Approaches for Generating Local Process Models through Log Projections	Local Process Model (LPM) discovery is focused on the mining of a set of process models where each model describes the behavior represented in the event log only partially, i.e. subsets of possible events are taken into account to create so-called local process models. Often such smaller models provide valuable insight...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	true	false	62,170
2212.11087	On Reinforcement Learning for the Game of 2048	2048 is a single-player stochastic puzzle game. This intriguing and addictive game has been popular worldwide and has attracted researchers to develop game-playing programs. Due to its simplicity and complexity, 2048 has become an interesting and challenging platform for evaluating the effectiveness of machine learning...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	337,688
2403.17931	Track Everything Everywhere Fast and Robustly	We propose a novel test-time optimization approach for efficiently and robustly tracking any pixel at any time in a video. The latest state-of-the-art optimization-based tracking technique, OmniMotion, requires a prohibitively long optimization time, rendering it impractical for downstream applications. OmniMotion is s...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	441,701
2006.11431	Band-limited Soft Actor Critic Model	Soft Actor Critic (SAC) algorithms show remarkable performance in complex simulated environments. A key element of SAC networks is entropy regularization, which prevents the SAC actor from optimizing against fine grained features, oftentimes transient, of the state-action value function. This results in better sample e...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	183,227
1909.01064	Face-to-Parameter Translation for Game Character Auto-Creation	Character customization system is an important component in Role-Playing Games (RPGs), where players are allowed to edit the facial appearance of their in-game characters with their own preferences rather than using default templates. This paper proposes a method for automatically creating in-game characters of players...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	143,799
1710.11383	Flexible Prior Distributions for Deep Generative Models	We consider the problem of training generative models with deep neural networks as generators, i.e. to map latent codes to data points. Whereas the dominant paradigm combines simple priors over codes with complex deterministic models, we argue that it might be advantageous to use more flexible code distributions. We de...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	83,586
2201.12819	A Safety-Critical Decision Making and Control Framework Combining Machine Learning and Rule-based Algorithms	While artificial-intelligence-based methods suffer from lack of transparency, rule-based methods dominate in safety-critical systems. Yet, the latter cannot compete with the first ones in robustness to multiple requirements, for instance, simultaneously addressing safety, comfort, and efficiency. Hence, to benefit from...	false	false	false	false	true	false	true	true	false	false	true	false	false	false	false	false	false	false	277,793
2411.01172	Covariance-based Space Regularization for Few-shot Class Incremental Learning	Few-shot Class Incremental Learning (FSCIL) presents a challenging yet realistic scenario, which requires the model to continually learn new classes with limited labeled data (i.e., incremental sessions) while retaining knowledge of previously learned base classes (i.e., base sessions). Due to the limited data in incre...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	504,945
1604.07044	Analyzing User Preference for Social Image Recommendation	With the incredibly growing amount of multimedia data shared on the social media platforms, recommender systems have become an important necessity to ease users' burden on the information overload. In such a scenario, extensive amount of heterogeneous information such as tags, image content, in addition to the user-to-...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	55,028
1906.04586	Proposition d'une nouvelle approche d'extraction des motifs ferm\'es fr\'equents	This work is done as part of a master's thesis project. The increase in the volume of data has given rise to various issues related to the collection, storage, analysis and exploitation of these data in order to create an added value. In this master, we are interested in the search of frequent closed patterns in the tr...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	134,767
2212.00523	The Limits of Learning and Planning: Minimal Sufficient Information Transition Systems	In this paper, we view a policy or plan as a transition system over a space of information states that reflect a robot's or other observer's perspective based on limited sensing, memory, computation, and actuation. Regardless of whether policies are obtained by learning algorithms, planning algorithms, or human insight...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	334,090
2205.13621	Differentially Private Decoding in Large Language Models	Recent large-scale natural language processing (NLP) systems use a pre-trained Large Language Model (LLM) on massive and diverse corpora as a headstart. In practice, the pre-trained model is adapted to a wide array of tasks via fine-tuning on task-specific datasets. LLMs, while effective, have been shown to memorize in...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	299,011
2104.09793	What is Wrong with One-Class Anomaly Detection?	From a safety perspective, a machine learning method embedded in real-world applications is required to distinguish irregular situations. For this reason, there has been a growing interest in the anomaly detection (AD) task. Since we cannot observe abnormal samples for most of the cases, recent AD methods attempt to fo...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	231,359
1512.09251	Solving the G-problems in less than 500 iterations: Improved efficient constrained optimization by surrogate modeling and adaptive parameter control	Constrained optimization of high-dimensional numerical problems plays an important role in many scientific and industrial applications. Function evaluations in many industrial applications are severely limited and no analytical information about objective function and constraint functions is available. For such expensi...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	50,577
2207.08372	Correcting $k$ Deletions and Insertions in Racetrack Memory	One of the main challenges in developing racetrack memory systems is the limited precision in controlling the track shifts, that in turn affects the reliability of reading and writing the data. A current proposal for combating deletions in racetrack memories is to use redundant heads per-track resulting in multiple cop...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	308,567
2311.18495	Improving Adversarial Transferability via Model Alignment	Neural networks are susceptible to adversarial perturbations that are transferable across different models. In this paper, we introduce a novel model alignment technique aimed at improving a given source model's ability in generating transferable adversarial perturbations. During the alignment process, the parameters o...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	411,699
1905.05961	Demographic Inference and Representative Population Estimates from Multilingual Social Media Data	Social media provide access to behavioural data at an unprecedented scale and granularity. However, using these data to understand phenomena in a broader population is difficult due to their non-representativeness and the bias of statistical inference tools towards dominant languages and groups. While demographic attri...	false	false	false	false	false	false	true	false	true	false	false	true	false	true	false	false	false	false	130,870
1910.05366	Learning Nearly Decomposable Value Functions Via Communication Minimization	Reinforcement learning encounters major challenges in multi-agent settings, such as scalability and non-stationarity. Recently, value function factorization learning emerges as a promising way to address these challenges in collaborative multi-agent systems. However, existing methods have been focusing on learning full...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	149,032
2410.09999	Leveraging Customer Feedback for Multi-modal Insight Extraction	Businesses can benefit from customer feedback in different modalities, such as text and images, to enhance their products and services. However, it is difficult to extract actionable and relevant pairs of text segments and images from customer feedback in a single pass. In this paper, we propose a novel multi-modal met...	false	false	false	false	true	true	false	false	true	false	false	true	false	false	false	false	false	false	497,864
2008.09644	Blending of Learning-based Tracking and Object Detection for Monocular Camera-based Target Following	Deep learning has recently started being applied to visual tracking of generic objects in video streams. For the purposes of robotics applications, it is very important for a target tracker to recover its track if it is lost due to heavy or prolonged occlusions or motion blur of the target. We present a real-time appro...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	192,771
2003.11844	P $\approx$ NP, at least in Visual Question Answering	In recent years, progress in the Visual Question Answering (VQA) field has largely been driven by public challenges and large datasets. One of the most widely-used of these is the VQA 2.0 dataset, consisting of polar ("yes/no") and non-polar questions. Looking at the question distribution over all answers, we find that...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	169,738
2305.03530	Exploring Softly Masked Language Modelling for Controllable Symbolic Music Generation	This document presents some early explorations of applying Softly Masked Language Modelling (SMLM) to symbolic music generation. SMLM can be seen as a generalisation of masked language modelling (MLM), where instead of each element of the input set being either known or unknown, each element can be known, unknown or pa...	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	362,432
1909.13611	MonoNet: Towards Interpretable Models by Learning Monotonic Features	Being able to interpret, or explain, the predictions made by a machine learning model is of fundamental importance. This is especially true when there is interest in deploying data-driven models to make high-stakes decisions, e.g. in healthcare. While recent years have seen an increasing interest in interpretable machi...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	147,478
2309.06825	Topology-inspired Cross-domain Network for Developmental Cervical Stenosis Quantification	Developmental Canal Stenosis (DCS) quantification is crucial in cervical spondylosis screening. Compared with quantifying DCS manually, a more efficient and time-saving manner is provided by deep keypoint localization networks, which can be implemented in either the coordinate or the image domain. However, the vertebra...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	391,561

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