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
2307.05884	Learning Koopman Operators with Control Using Bi-level Optimization	The accurate modeling and control of nonlinear dynamical effects are crucial for numerous robotic systems. The Koopman formalism emerges as a valuable tool for linear control design in nonlinear systems within unknown environments. However, it still remains a challenging task to learn the Koopman operator with control ...	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	378,884
2408.06610	CROME: Cross-Modal Adapters for Efficient Multimodal LLM	Multimodal Large Language Models (MLLMs) demonstrate remarkable image-language capabilities, but their widespread use faces challenges in cost-effective training and adaptation. Existing approaches often necessitate expensive language model retraining and limited adaptability. Additionally, the current focus on zero-sh...	false	false	false	false	false	false	true	false	true	false	false	true	false	false	false	false	false	false	480,263
2007.09762	A Theory of Multiple-Source Adaptation with Limited Target Labeled Data	We present a theoretical and algorithmic study of the multiple-source domain adaptation problem in the common scenario where the learner has access only to a limited amount of labeled target data, but where the learner has at disposal a large amount of labeled data from multiple source domains. We show that a new famil...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	188,059
1909.09143	Leveraging User Engagement Signals For Entity Labeling in a Virtual Assistant	Personal assistant AI systems such as Siri, Cortana, and Alexa have become widely used as a means to accomplish tasks through natural language commands. However, components in these systems generally rely on supervised machine learning algorithms that require large amounts of hand-annotated training data, which is expe...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	146,162
1604.01476	Zadoff-Chu sequence design for random access initial uplink synchronization	The autocorrelation of a Zadoff-Chu (ZC) sequence with a non-zero cyclically shifted version of itself is zero. Due to the interesting property, ZC sequences are widely used in the LTE air interface in the primary synchronization signal (PSS), random access preamble (PRACH), uplink control channel (PUCCH) etc. However,...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	54,203
2407.20413	Through the Looking Glass, and what Horn Clause Programs Found There	Dual Horn clauses mirror key properties of Horn clauses. This paper explores the ``other side of the looking glass'' to reveal some expected and unexpected symmetries and their practical uses. We revisit Dual Horn clauses as enablers of a form of constructive negation that supports goal-driven forward reasoning and i...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	477,154
2405.02525	RLStop: A Reinforcement Learning Stopping Method for TAR	We present RLStop, a novel Technology Assisted Review (TAR) stopping rule based on reinforcement learning that helps minimise the number of documents that need to be manually reviewed within TAR applications. RLStop is trained on example rankings using a reward function to identify the optimal point to stop examining d...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	451,797
2405.14226	Variational Delayed Policy Optimization	In environments with delayed observation, state augmentation by including actions within the delay window is adopted to retrieve Markovian property to enable reinforcement learning (RL). However, state-of-the-art (SOTA) RL techniques with Temporal-Difference (TD) learning frameworks often suffer from learning inefficie...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	456,319
2102.00573	A Secure Learning Control Strategy via Dynamic Camouflaging for Unknown Dynamical Systems under Attacks	This paper presents a secure reinforcement learning (RL) based control method for unknown linear time-invariant cyber-physical systems (CPSs) that are subjected to compositional attacks such as eavesdropping and covert attack. We consider the attack scenario where the attacker learns about the dynamic model during the ...	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	217,834
1809.07744	Guaranteed Globally Optimal Planar Pose Graph and Landmark SLAM via Sparse-Bounded Sums-of-Squares Programming	Autonomous navigation requires an accurate model or map of the environment. While dramatic progress in the prior two decades has enabled large-scale SLAM, the majority of existing methods rely on non-linear optimization techniques to find the MLE of the robot trajectory and surrounding environment. These methods are pr...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	108,345
1801.03331	Reasoning about Unforeseen Possibilities During Policy Learning	Methods for learning optimal policies in autonomous agents often assume that the way the domain is conceptualised---its possible states and actions and their causal structure---is known in advance and does not change during learning. This is an unrealistic assumption in many scenarios, because new evidence can reveal i...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	88,074
2102.02640	Low Bit-Rate Wideband Speech Coding: A Deep Generative Model based Approach	Traditional low bit-rate speech coding approach only handles narrowband speech at 8kHz, which limits further improvements in speech quality. Motivated by recent successful exploration of deep learning methods for image and speech compression, this paper presents a new approach through vector quantization (VQ) of mel-fr...	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	218,474
2408.10575	MUSE: Mamba is Efficient Multi-scale Learner for Text-video Retrieval	Text-Video Retrieval (TVR) aims to align and associate relevant video content with corresponding natural language queries. Most existing TVR methods are based on large-scale pre-trained vision-language models (e.g., CLIP). However, due to the inherent plain structure of CLIP, few TVR methods explore the multi-scale rep...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	481,920
1710.02186	Collaborative Platooning of Automated Vehicles Using Variable Time-Gaps	Connected automated vehicles (CAVs) could potentially be coordinated to safely attain the maximum traffic flow on roadways under dynamic traffic patterns, such as those engendered by the merger of two strings of vehicles due a lane drop. Strings of vehicles have to be shaped correctly in terms of the inter-vehicular ti...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	82,124
2304.02497	Hyper-parameter Tuning for Adversarially Robust Models	This work focuses on the problem of hyper-parameter tuning (HPT) for robust (i.e., adversarially trained) models, shedding light on the new challenges and opportunities arising during the HPT process for robust models. To this end, we conduct an extensive experimental study based on 3 popular deep models, in which we e...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	356,455
2405.03699	HCC Is All You Need: Alignment-The Sensible Kind Anyway-Is Just Human-Centered Computing	This article argues that AI Alignment is a type of Human-Centered Computing.	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	452,282
2310.12942	On the Representational Capacity of Recurrent Neural Language Models	This work investigates the computational expressivity of language models (LMs) based on recurrent neural networks (RNNs). Siegelmann and Sontag (1992) famously showed that RNNs with rational weights and hidden states and unbounded computation time are Turing complete. However, LMs define weightings over strings in addi...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	401,210
1910.03620	Receding Horizon Curiosity	Sample-efficient exploration is crucial not only for discovering rewarding experiences but also for adapting to environment changes in a task-agnostic fashion. A principled treatment of the problem of optimal input synthesis for system identification is provided within the framework of sequential Bayesian experimental ...	false	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	148,537
2407.19156	Robust Multimodal 3D Object Detection via Modality-Agnostic Decoding and Proximity-based Modality Ensemble	Recent advancements in 3D object detection have benefited from multi-modal information from the multi-view cameras and LiDAR sensors. However, the inherent disparities between the modalities pose substantial challenges. We observe that existing multi-modal 3D object detection methods heavily rely on the LiDAR sensor, t...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	476,665
2009.02023	Chain-Net: Learning Deep Model for Modulation Classification Under Synthetic Channel Impairment	Modulation classification, an intermediate process between signal detection and demodulation in a physical layer, is now attracting more interest to the cognitive radio field, wherein the performance is powered by artificial intelligence algorithms. However, most existing conventional approaches pose the obstacle of ef...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	194,444
2001.06804	Learning Compositional Neural Information Fusion for Human Parsing	This work proposes to combine neural networks with the compositional hierarchy of human bodies for efficient and complete human parsing. We formulate the approach as a neural information fusion framework. Our model assembles the information from three inference processes over the hierarchy: direct inference (directly p...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	160,887
2302.07667	CERiL: Continuous Event-based Reinforcement Learning	This paper explores the potential of event cameras to enable continuous time reinforcement learning. We formalise this problem where a continuous stream of unsynchronised observations is used to produce a corresponding stream of output actions for the environment. This lack of synchronisation enables greatly enhanced r...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	345,796
2407.11553	Learning Global and Local Features of Power Load Series Through Transformer and 2D-CNN: An Image-based Multi-step Forecasting Approach Incorporating Phase Space Reconstruction	As modern power systems continue to evolve, accurate power load forecasting remains a critical issue in energy management. The phase space reconstruction method can effectively retain the inner chaotic property of power load from a system dynamics perspective and thus is a promising knowledge-based preprocessing method...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	473,514
2305.14992	Reasoning with Language Model is Planning with World Model	Large language models (LLMs) have shown remarkable reasoning capabilities, especially when prompted to generate intermediate reasoning steps (e.g., Chain-of-Thought, CoT). However, LLMs can still struggle with problems that are easy for humans, such as generating action plans for executing tasks in a given environment,...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	367,378
2112.13833	HOPE: A Task-Oriented and Human-Centric Evaluation Framework Using Professional Post-Editing Towards More Effective MT Evaluation	Traditional automatic evaluation metrics for machine translation have been widely criticized by linguists due to their low accuracy, lack of transparency, focus on language mechanics rather than semantics, and low agreement with human quality evaluation. Human evaluations in the form of MQM-like scorecards have always ...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	273,370
2112.00890	Counterfactual Explanations via Latent Space Projection and Interpolation	Counterfactual explanations represent the minimal change to a data sample that alters its predicted classification, typically from an unfavorable initial class to a desired target class. Counterfactuals help answer questions such as "what needs to change for this application to get accepted for a loan?". A number of re...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	269,283
2410.11298	Sorted Weight Sectioning for Energy-Efficient Unstructured Sparse DNNs on Compute-in-Memory Crossbars	We introduce $\textit{sorted weight sectioning}$ (SWS): a weight allocation algorithm that places sorted deep neural network (DNN) weight sections on bit-sliced compute-in-memory (CIM) crossbars to reduce analog-to-digital converter (ADC) energy consumption. Data conversions are the most energy-intensive process in cro...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	true	498,493
1309.7817	Performance Analysis of Massive MIMO for Cell-Boundary Users	In this paper, we consider massive multiple-input multiple-output (MIMO) systems for both downlink and uplink scenarios, where three radio units (RUs) connected via one digital unit (DU) support multiple user equipments (UEs) at the cell-boundary through the same radio resource, i.e., the same time-frequency slot. For ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	27,424
2209.11880	Real-Time Model Predictive Control for Industrial Manipulators with Singularity-Tolerant Hierarchical Task Control	This paper proposes a real-time model predictive control (MPC) scheme to execute multiple tasks using robots over a finite-time horizon. In industrial robotic applications, we must carefully consider multiple constraints for avoiding joint position, velocity, and torque limits. In addition, singularity-free and smooth ...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	319,328
2306.07996	Point spread function modelling for astronomical telescopes: a review focused on weak gravitational lensing studies	The accurate modelling of the Point Spread Function (PSF) is of paramount importance in astronomical observations, as it allows for the correction of distortions and blurring caused by the telescope and atmosphere. PSF modelling is crucial for accurately measuring celestial objects' properties. The last decades brought...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	373,242
1603.02729	Revisiting Active Perception	Despite the recent successes in robotics, artificial intelligence and computer vision, a complete artificial agent necessarily must include active perception. A multitude of ideas and methods for how to accomplish this have already appeared in the past, their broader utility perhaps impeded by insufficient computationa...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	53,046
2109.03780	Bayesian Over-The-Air Computation	As an important piece of the multi-tier computing architecture for future wireless networks, over-the-air computation (OAC) enables efficient function computation in multiple-access edge computing, where a fusion center aims to compute a function of the data distributed at edge devices. Existing OAC relies exclusively ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	254,164
2110.14322	Node-wise Localization of Graph Neural Networks	Graph neural networks (GNNs) emerge as a powerful family of representation learning models on graphs. To derive node representations, they utilize a global model that recursively aggregates information from the neighboring nodes. However, different nodes reside at different parts of the graph in different local context...	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	false	263,498
1907.08494	Performance evaluation of THz wireless systems under the joint impact of misalignment fading and phase noise	In this paper, we investigate the joint impact of misalignment fading and local oscillator (LO) phase noise (PHN) in multi-carrier terahertz (THz) wireless systems. In more detail, after establishing a suitable system model that takes into account the particularities of the THz channel, as well as the transceivers char...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	139,118
1607.05746	Bayesian Non-Exhaustive Classification A Case Study: Online Name Disambiguation using Temporal Record Streams	The name entity disambiguation task aims to partition the records of multiple real-life persons so that each partition contains records pertaining to a unique person. Most of the existing solutions for this task operate in a batch mode, where all records to be disambiguated are initially available to the algorithm. How...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	58,791
1507.03867	Rich Component Analysis	In many settings, we have multiple data sets (also called views) that capture different and overlapping aspects of the same phenomenon. We are often interested in finding patterns that are unique to one or to a subset of the views. For example, we might have one set of molecular observations and one set of physiologica...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	45,113
1511.04143	Deep Reinforcement Learning in Parameterized Action Space	Recent work has shown that deep neural networks are capable of approximating both value functions and policies in reinforcement learning domains featuring continuous state and action spaces. However, to the best of our knowledge no previous work has succeeded at using deep neural networks in structured (parameterized) ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	true	true	false	false	48,850
1905.12080	Non-normal Recurrent Neural Network (nnRNN): learning long time dependencies while improving expressivity with transient dynamics	A recent strategy to circumvent the exploding and vanishing gradient problem in RNNs, and to allow the stable propagation of signals over long time scales, is to constrain recurrent connectivity matrices to be orthogonal or unitary. This ensures eigenvalues with unit norm and thus stable dynamics and training. However ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	132,639
2502.02689	Multidimensional Swarm Flight Approach For Chasing Unauthorized UAVs Leveraging Asynchronous Deep Learning	This paper introduces a novel unmanned aerial vehicles (UAV) chasing system designed to track and chase unauthorized UAVs, significantly enhancing their neutralization effectiveness.	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	530,427
2501.02937	4D-CS: Exploiting Cluster Prior for 4D Spatio-Temporal LiDAR Semantic Segmentation	Semantic segmentation of LiDAR points has significant value for autonomous driving and mobile robot systems. Most approaches explore spatio-temporal information of multi-scan to identify the semantic classes and motion states for each point. However, these methods often overlook the segmentation consistency in space an...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	522,699
1810.02320	Computer vision-based framework for extracting geological lineaments from optical remote sensing data	The extraction of geological lineaments from digital satellite data is a fundamental application in remote sensing. The location of geological lineaments such as faults and dykes are of interest for a range of applications, particularly because of their association with hydrothermal mineralization. Although a wide rang...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	109,570
2307.01668	Training Energy-Based Models with Diffusion Contrastive Divergences	Energy-Based Models (EBMs) have been widely used for generative modeling. Contrastive Divergence (CD), a prevailing training objective for EBMs, requires sampling from the EBM with Markov Chain Monte Carlo methods (MCMCs), which leads to an irreconcilable trade-off between the computational burden and the validity of t...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	377,431
1912.13088	Off-Policy Estimation of Long-Term Average Outcomes with Applications to Mobile Health	Due to the recent advancements in wearables and sensing technology, health scientists are increasingly developing mobile health (mHealth) interventions. In mHealth interventions, mobile devices are used to deliver treatment to individuals as they go about their daily lives. These treatments are generally designed to im...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	159,002
2305.13300	Adaptive Chameleon or Stubborn Sloth: Revealing the Behavior of Large Language Models in Knowledge Conflicts	By providing external information to large language models (LLMs), tool augmentation (including retrieval augmentation) has emerged as a promising solution for addressing the limitations of LLMs' static parametric memory. However, how receptive are LLMs to such external evidence, especially when the evidence conflicts ...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	366,447
2010.09367	On Properties and Optimization of Information-theoretic Privacy Watchdog	We study the problem of privacy preservation in data sharing, where $S$ is a sensitive variable to be protected and $X$ is a non-sensitive useful variable correlated with $S$. Variable $X$ is randomized into variable $Y$, which will be shared or released according to $p_{Y\|X}(y\|x)$. We measure privacy leakage by \emph{...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	201,513
2011.12946	Exploratory LQG Mean Field Games with Entropy Regularization	We study a general class of entropy-regularized multi-variate LQG mean field games (MFGs) in continuous time with $K$ distinct sub-population of agents. We extend the notion of actions to action distributions (exploratory actions), and explicitly derive the optimal action distributions for individual agents in the limi...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	208,320
2403.04558	Reducing self-supervised learning complexity improves weakly-supervised classification performance in computational pathology	Deep Learning models have been successfully utilized to extract clinically actionable insights from routinely available histology data. Generally, these models require annotations performed by clinicians, which are scarce and costly to generate. The emergence of self-supervised learning (SSL) methods remove this barrie...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	435,643
2405.01249	Prompt engineering paradigms for medical applications: scoping review and recommendations for better practices	Prompt engineering is crucial for harnessing the potential of large language models (LLMs), especially in the medical domain where specialized terminology and phrasing is used. However, the efficacy of prompt engineering in the medical domain remains to be explored. In this work, 114 recent studies (2022-2024) applying...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	451,286
2301.05336	Multitask Weakly Supervised Learning for Origin Destination Travel Time Estimation	Travel time estimation from GPS trips is of great importance to order duration, ridesharing, taxi dispatching, etc. However, the dense trajectory is not always available due to the limitation of data privacy and acquisition, while the origin destination (OD) type of data, such as NYC taxi data, NYC bike data, and Capit...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	340,323
2209.09631	De-Identification of French Unstructured Clinical Notes for Machine Learning Tasks	Unstructured textual data are at the heart of health systems: liaison letters between doctors, operating reports, coding of procedures according to the ICD-10 standard, etc. The details included in these documents make it possible to get to know the patient better, to better manage him or her, to better study the patho...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	318,581
2112.07794	Review of Factor Graphs for Robust GNSS Applications	Factor graphs have recently emerged as an alternative solution method for GNSS positioning. In this article, we review how factor graphs are implemented in GNSS, some of their advantages over Kalman Filters, and their importance in making positioning solutions more robust to degraded measurements. We also talk about ho...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	271,586
1709.03947	Constant Space Complexity Environment Representation for Vision-based Navigation	This paper presents a preliminary conceptual investigation into an environment representation that has constant space complexity with respect to the camera image space. This type of representation allows the planning algorithms of a mobile agent to bypass what are often complex and noisy transformations between camera ...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	80,570
2409.17758	Adapting Deep Variational Bayes Filter for Enhanced Confidence Estimation in Finite Element Method Integrated Networks (FEMIN)	The Finite Element Method (FEM) is a widely used technique for simulating crash scenarios with high accuracy and reliability. To reduce the significant computational costs associated with FEM, the Finite Element Method Integrated Networks (FEMIN) framework integrates neural networks (NNs) with FEM solvers. However, thi...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	491,969
2306.17829	Federated Ensemble YOLOv5 -- A Better Generalized Object Detection Algorithm	Federated learning (FL) has gained significant traction as a privacy-preserving algorithm, but the underlying resemblances of federated learning algorithms like Federated averaging (FedAvg) or Federated SGD (Fed SGD) to ensemble learning algorithms have not been fully explored. The purpose of this paper is to examine t...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	376,823
2409.03189	A note on the differential spectrum of the Ness-Helleseth function	Let $n\geqslant3$ be an odd integer and $u$ an element in the finite field $\gf_{3^n}$. The Ness-Helleseth function is the binomial $f_u(x)=ux^{d_1}+x^{d_2}$ over $\gf_{3^n}$, where $d_1=\frac{3^n-1}{2}-1$ and $d_2=3^n-2$. In 2007, Ness and Helleseth showed that $f_u$ is an APN function when $\chi(u+1)=\chi(u-1)=\chi(u...	false	false	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	485,951
2402.00449	Parallel Spiking Unit for Efficient Training of Spiking Neural Networks	Efficient parallel computing has become a pivotal element in advancing artificial intelligence. Yet, the deployment of Spiking Neural Networks (SNNs) in this domain is hampered by their inherent sequential computational dependency. This constraint arises from the need for each time step's processing to rely on the prec...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	425,615
2410.05269	Data Advisor: Dynamic Data Curation for Safety Alignment of Large Language Models	Data is a crucial element in large language model (LLM) alignment. Recent studies have explored using LLMs for efficient data collection. However, LLM-generated data often suffers from quality issues, with underrepresented or absent aspects and low-quality datapoints. To address these problems, we propose Data Advisor,...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	495,644
1903.01620	What to Expect of Classifiers? Reasoning about Logistic Regression with Missing Features	While discriminative classifiers often yield strong predictive performance, missing feature values at prediction time can still be a challenge. Classifiers may not behave as expected under certain ways of substituting the missing values, since they inherently make assumptions about the data distribution they were train...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	123,299
2404.13973	DEQ-MCL: Discrete-Event Queue-based Monte-Carlo Localization	Spatial cognition in hippocampal formation is posited to play a crucial role in the development of self-localization techniques for robots. In this paper, we propose a self-localization approach, DEQ-MCL, based on the discrete event queue hypothesis associated with phase precession within the hippocampal formation. Our...	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	448,520
2403.01431	Image2Sentence based Asymmetrical Zero-shot Composed Image Retrieval	The task of composed image retrieval (CIR) aims to retrieve images based on the query image and the text describing the users' intent. Existing methods have made great progress with the advanced large vision-language (VL) model in CIR task, however, they generally suffer from two main issues: lack of labeled triplets f...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	434,412
2107.01057	Backward-Compatible Prediction Updates: A Probabilistic Approach	When machine learning systems meet real world applications, accuracy is only one of several requirements. In this paper, we assay a complementary perspective originating from the increasing availability of pre-trained and regularly improving state-of-the-art models. While new improved models develop at a fast pace, dow...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	244,361
2303.15433	Anti-DreamBooth: Protecting users from personalized text-to-image synthesis	Text-to-image diffusion models are nothing but a revolution, allowing anyone, even without design skills, to create realistic images from simple text inputs. With powerful personalization tools like DreamBooth, they can generate images of a specific person just by learning from his/her few reference images. However, wh...	false	false	false	false	false	false	true	false	false	false	false	true	true	false	false	false	false	false	354,489
2403.10290	Offline Goal-Conditioned Reinforcement Learning for Shape Control of Deformable Linear Objects	Deformable objects present several challenges to the field of robotic manipulation. One of the tasks that best encapsulates the difficulties arising due to non-rigid behavior is shape control, which requires driving an object to a desired shape. While shape-servoing methods have been shown successful in contexts with a...	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	438,133
2310.20246	Breaking Language Barriers in Multilingual Mathematical Reasoning: Insights and Observations	Existing research predominantly focuses on developing powerful language learning models (LLMs) for mathematical reasoning within monolingual languages, with few explorations in preserving efficacy in a multilingual context. To bridge this gap, this paper pioneers exploring and training powerful Multilingual Math Reason...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	404,317
2304.06412	Quantifying and Explaining Machine Learning Uncertainty in Predictive Process Monitoring: An Operations Research Perspective	This paper introduces a comprehensive, multi-stage machine learning methodology that effectively integrates information systems and artificial intelligence to enhance decision-making processes within the domain of operations research. The proposed framework adeptly addresses common limitations of existing solutions, su...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	357,970
1304.1086	Integrating Probabilistic, Taxonomic and Causal Knowledge in Abductive Diagnosis	We propose an abductive diagnosis theory that integrates probabilistic, causal and taxonomic knowledge. Probabilistic knowledge allows us to select the most likely explanation; causal knowledge allows us to make reasonable independence assumptions; taxonomic knowledge allows causation to be modeled at different levels ...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	23,439
2011.01046	NEARL: Non-Explicit Action Reinforcement Learning for Robotic Control	Traditionally, reinforcement learning methods predict the next action based on the current state. However, in many situations, directly applying actions to control systems or robots is dangerous and may lead to unexpected behaviors because action is rather low-level. In this paper, we propose a novel hierarchical reinf...	false	false	false	false	true	false	true	true	false	false	false	false	false	false	false	false	false	false	204,463
2402.06921	Clustering Techniques Selection for a Hybrid Regression Model: A Case Study Based on a Solar Thermal System	This work addresses the performance comparison between four clustering techniques with the objective of achieving strong hybrid models in supervised learning tasks. A real dataset from a bio-climatic house named Sotavento placed on experimental wind farm and located in Xermade (Lugo) in Galicia (Spain) has been collect...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	false	false	false	428,487
2308.05739	Zero Grads: Learning Local Surrogate Losses for Non-Differentiable Graphics	Gradient-based optimization is now ubiquitous across graphics, but unfortunately can not be applied to problems with undefined or zero gradients. To circumvent this issue, the loss function can be manually replaced by a ``surrogate'' that has similar minima but is differentiable. Our proposed framework, ZeroGrads, auto...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	true	384,891
2211.10764	Understanding the Bystander Effect on Toxic Twitter Conversations	In this study, we explore the power of group dynamics to shape the toxicity of Twitter conversations. First, we examine how the presence of others in a conversation can potentially diffuse Twitter users' responsibility to address a toxic direct reply. Second, we examine whether the toxicity of the first direct reply to...	false	false	false	true	false	false	false	false	false	false	false	false	false	true	false	false	false	false	331,429
2407.02547	Domain Generalizable Knowledge Tracing via Concept Aggregation and Relation-Based Attention	Knowledge Tracing (KT) is a critical task in online education systems, aiming to monitor students' knowledge states throughout a learning period. Common KT approaches involve predicting the probability of a student correctly answering the next question based on their exercise history. However, these methods often suffe...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	469,790
1312.6978	Mod\`ele \`a processus latent et algorithme EM pour la r\'egression non lin\'eaire	A non linear regression approach which consists of a specific regression model incorporating a latent process, allowing various polynomial regression models to be activated preferentially and smoothly, is introduced in this paper. The model parameters are estimated by maximum likelihood performed via a dedicated expeca...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	29,433
2203.14806	Extraction of Visual Information to Predict Crowdfunding Success	Researchers have increasingly turned to crowdfunding platforms to gain insights into entrepreneurial activity and dynamics. While previous studies have explored various factors influencing crowdfunding success, such as technology, communication, and marketing strategies, the role of visual elements that can be automati...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	288,136
2306.12343	Quantum R\'enyi and $f$-divergences from integral representations	Smooth Csisz\'ar $f$-divergences can be expressed as integrals over so-called hockey stick divergences. This motivates a natural quantum generalization in terms of quantum Hockey stick divergences, which we explore here. Using this recipe, the Kullback-Leibler divergence generalises to the Umegaki relative entropy, in ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	374,909
2008.06266	Optimized Deep Encoder-Decoder Methods for Crack Segmentation	Surface crack segmentation poses a challenging computer vision task as background, shape, colour and size of cracks vary. In this work we propose optimized deep encoder-decoder methods consisting of a combination of techniques which yield an increase in crack segmentation performance. Specifically we propose a decoder-...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	191,755
1507.05681	A Tractable Analysis of the Improvement in Unique Localizability Through Collaboration	In this paper, we mathematically characterize the improvement in device localizability achieved by allowing collaboration among devices. Depending on the detection sensitivity of the receivers in the devices, it is not unusual for a device to be localized to lack a sufficient number of detectable positioning signals fr...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	45,310
2303.17158	KD-DLGAN: Data Limited Image Generation via Knowledge Distillation	Generative Adversarial Networks (GANs) rely heavily on large-scale training data for training high-quality image generation models. With limited training data, the GAN discriminator often suffers from severe overfitting which directly leads to degraded generation especially in generation diversity. Inspired by the rece...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	355,125
2311.06311	Game Theory Solutions in Sensor-Based Human Activity Recognition: A Review	The Human Activity Recognition (HAR) tasks automatically identify human activities using the sensor data, which has numerous applications in healthcare, sports, security, and human-computer interaction. Despite significant advances in HAR, critical challenges still exist. Game theory has emerged as a promising solution...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	true	406,895
2305.19713	Red Teaming Language Model Detectors with Language Models	The prevalence and strong capability of large language models (LLMs) present significant safety and ethical risks if exploited by malicious users. To prevent the potentially deceptive usage of LLMs, recent works have proposed algorithms to detect LLM-generated text and protect LLMs. In this paper, we investigate the ro...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	369,651
2108.08090	CollaborER: A Self-supervised Entity Resolution Framework Using Multi-features Collaboration	Entity Resolution (ER) aims to identify whether two tuples refer to the same real-world entity and is well-known to be labor-intensive. It is a prerequisite to anomaly detection, as comparing the attribute values of two matched tuples from two different datasets provides one effective way to detect anomalies. Existing ...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	251,131
2003.07849	Blur, Noise, and Compression Robust Generative Adversarial Networks	Generative adversarial networks (GANs) have gained considerable attention owing to their ability to reproduce images. However, they can recreate training images faithfully despite image degradation in the form of blur, noise, and compression, generating similarly degraded images. To solve this problem, the recently pro...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	168,561
2307.12661	Algorithmic construction of Lyapunov functions for continuous vector fields via convex semi-infinite programs	This article presents a novel numerically tractable technique for synthesizing Lyapunov functions for equilibria of nonlinear vector fields. In broad strokes, corresponding to an isolated equilibrium point of a given vector field, a selection is made of a compact neighborhood of the equilibrium and a dictionary of func...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	381,339
1801.09936	PEYMA: A Tagged Corpus for Persian Named Entities	The goal in the NER task is to classify proper nouns of a text into classes such as person, location, and organization. This is an important preprocessing step in many NLP tasks such as question-answering and summarization. Although many research studies have been conducted in this area in English and the state-of-the-...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	89,199
1806.06545	A Simple Reservoir Model of Working Memory with Real Values	The prefrontal cortex is known to be involved in many high-level cognitive functions, in particular, working memory. Here, we study to what extent a group of randomly connected units (namely an Echo State Network, ESN) can store and maintain (as output) an arbitrary real value from a streamed input, i.e. can act as a s...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	100,726
1912.10204	A Machine Learning Framework for Authorship Identification From Texts	Authorship identification is a process in which the author of a text is identified. Most known literary texts can easily be attributed to a certain author because they are, for example, signed. Yet sometimes we find unfinished pieces of work or a whole bunch of manuscripts with a wide variety of possible authors. In or...	false	false	false	false	false	true	true	false	true	false	false	false	false	false	false	false	false	false	158,264
2305.02693	Semi-supervised Domain Adaptation via Prototype-based Multi-level Learning	In semi-supervised domain adaptation (SSDA), a few labeled target samples of each class help the model to transfer knowledge representation from the fully labeled source domain to the target domain. Many existing methods ignore the benefits of making full use of the labeled target samples from multi-level. To make bett...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	362,136
2207.06741	Differentiable Logics for Neural Network Training and Verification	The rising popularity of neural networks (NNs) in recent years and their increasing prevalence in real-world applications have drawn attention to the importance of their verification. While verification is known to be computationally difficult theoretically, many techniques have been proposed for solving it in practice...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	true	307,977
2203.07643	Can Synthetic Translations Improve Bitext Quality?	Synthetic translations have been used for a wide range of NLP tasks primarily as a means of data augmentation. This work explores, instead, how synthetic translations can be used to revise potentially imperfect reference translations in mined bitext. We find that synthetic samples can improve bitext quality without any...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	285,498
1603.03101	Recursive Recurrent Nets with Attention Modeling for OCR in the Wild	We present recursive recurrent neural networks with attention modeling (R$^2$AM) for lexicon-free optical character recognition in natural scene images. The primary advantages of the proposed method are: (1) use of recursive convolutional neural networks (CNNs), which allow for parametrically efficient and effective im...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	53,076
2010.11148	FastEmit: Low-latency Streaming ASR with Sequence-level Emission Regularization	Streaming automatic speech recognition (ASR) aims to emit each hypothesized word as quickly and accurately as possible. However, emitting fast without degrading quality, as measured by word error rate (WER), is highly challenging. Existing approaches including Early and Late Penalties and Constrained Alignments penaliz...	false	false	true	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	202,138
2006.11513	Deep Learning based Radio Resource Management in NOMA Networks: User Association, Subchannel and Power Allocation	With the rapid development of future wireless communication, the combination of NOMA technology and millimeter-wave(mmWave) technology has become a research hotspot. The application of NOMA in mmWave heterogeneous networks can meet the diverse needs of users in different applications and scenarios in future communicati...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	183,268
2411.02149	Improving Domain Generalization in Self-supervised Monocular Depth Estimation via Stabilized Adversarial Training	Learning a self-supervised Monocular Depth Estimation (MDE) model with great generalization remains significantly challenging. Despite the success of adversarial augmentation in the supervised learning generalization, naively incorporating it into self-supervised MDE models potentially causes over-regularization, suffe...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	505,376
2410.12971	Self-Pluralising Culture Alignment for Large Language Models	As large language models (LLMs) become increasingly accessible in many countries, it is essential to align them to serve pluralistic human values across cultures. However, pluralistic culture alignment in LLMs remain an open problem. In this paper, we propose CultureSPA, a Self-Pluralising Culture Alignment framework t...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	499,302
2406.16976	Efficient Evolutionary Search Over Chemical Space with Large Language Models	Molecular discovery, when formulated as an optimization problem, presents significant computational challenges because optimization objectives can be non-differentiable. Evolutionary Algorithms (EAs), often used to optimize black-box objectives in molecular discovery, traverse chemical space by performing random mutati...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	true	false	false	467,375
1804.04168	Differentiable Learning of Quantum Circuit Born Machine	Quantum circuit Born machines are generative models which represent the probability distribution of classical dataset as quantum pure states. Computational complexity considerations of the quantum sampling problem suggest that the quantum circuits exhibit stronger expressibility compared to classical neural networks. O...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	94,769
1811.12787	A Tutorial for Weighted Bipolar Argumentation with Continuous Dynamical Systems and the Java Library Attractor	Weighted bipolar argumentation frameworks allow modeling decision problems and online discussions by defining arguments and their relationships. The strength of arguments can be computed based on an initial weight and the strength of attacking and supporting arguments. While previous approaches assumed an acyclic argum...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	115,095
1305.6003	Exploiting Self-Interference Suppression for Improved Spectrum Awareness/Efficiency in Cognitive Radio Systems	Inspired by recent developments in full-duplex communications, we propose and study new modes of operation for cognitive radios with the goal of achieving improved primary user (PU) detection and/or secondary user (SU) throughput. Specifically, we consider an opportunistic PU/SU setting in which the SU is equipped with...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	24,809
2407.10640	Error Bounds for the Network Scale-Up Method	Epidemiologists and social scientists have used the Network Scale-Up Method (NSUM) for over thirty years to estimate the size of a hidden sub-population within a social network. This method involves querying a subset of network nodes about the number of their neighbours belonging to the hidden sub-population. In genera...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	true	473,061
2412.10392	Computational Methods for Breast Cancer Molecular Profiling through Routine Histopathology: A Review	Precision medicine has become a central focus in breast cancer management, advancing beyond conventional methods to deliver more precise and individualized therapies. Traditionally, histopathology images have been used primarily for diagnostic purposes; however, they are now recognized for their potential in molecular ...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	516,898
2411.09268	LES-Talker: Fine-Grained Emotion Editing for Talking Head Generation in Linear Emotion Space	While existing one-shot talking head generation models have achieved progress in coarse-grained emotion editing, there is still a lack of fine-grained emotion editing models with high interpretability. We argue that for an approach to be considered fine-grained, it needs to provide clear definitions and sufficiently de...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	508,193

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