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
2408.14141	Crowd-Calibrator: Can Annotator Disagreement Inform Calibration in Subjective Tasks?	Subjective tasks in NLP have been mostly relegated to objective standards, where the gold label is decided by taking the majority vote. This obfuscates annotator disagreement and the inherent uncertainty of the label. We argue that subjectivity should factor into model decisions and play a direct role via calibration u...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	483,432
2203.02491	Performance of Large Aperture UCCA Arrays in a 5G User Dense Network	The transmitted signals in the fifth generation (5G) wireless networks suffer from significant path loss due to the use of higher frequencies in Sub-6 GHz and millimeter-wave (mmWave) bands. Inter-user interference in an ultra-dense network offers additional challenges to provide a high data rate. Therefore, it is desi...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	283,764
2304.05336	Exploring the Use of Foundation Models for Named Entity Recognition and Lemmatization Tasks in Slavic Languages	This paper describes Adam Mickiewicz University's (AMU) solution for the 4th Shared Task on SlavNER. The task involves the identification, categorization, and lemmatization of named entities in Slavic languages. Our approach involved exploring the use of foundation models for these tasks. In particular, we used models ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	357,580
2112.11953	Text is no more Enough! A Benchmark for Profile-based Spoken Language Understanding	Current researches on spoken language understanding (SLU) heavily are limited to a simple setting: the plain text-based SLU that takes the user utterance as input and generates its corresponding semantic frames (e.g., intent and slots). Unfortunately, such a simple setting may fail to work in complex real-world scenari...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	272,844
2303.05321	WASD: A Wilder Active Speaker Detection Dataset	Current Active Speaker Detection (ASD) models achieve great results on AVA-ActiveSpeaker (AVA), using only sound and facial features. Although this approach is applicable in movie setups (AVA), it is not suited for less constrained conditions. To demonstrate this limitation, we propose a Wilder Active Speaker Detection...	false	false	true	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	350,421
1910.00760	Efficient Graph Generation with Graph Recurrent Attention Networks	We propose a new family of efficient and expressive deep generative models of graphs, called Graph Recurrent Attention Networks (GRANs). Our model generates graphs one block of nodes and associated edges at a time. The block size and sampling stride allow us to trade off sample quality for efficiency. Compared to previ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	147,761
2502.00409	Doing More with Less -- Implementing Routing Strategies in Large Language Model-Based Systems: An Extended Survey	Large Language Models (LLM)-based systems, i.e. interconnected elements that include an LLM as a central component (e.g., conversational agents), are typically monolithic static architectures that rely on a single LLM for all user queries. However, they often require different preprocessing strategies, levels of reason...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	529,369
2206.03520	Finite-Time Regret of Thompson Sampling Algorithms for Exponential Family Multi-Armed Bandits	We study the regret of Thompson sampling (TS) algorithms for exponential family bandits, where the reward distribution is from a one-dimensional exponential family, which covers many common reward distributions including Bernoulli, Gaussian, Gamma, Exponential, etc. We propose a Thompson sampling algorithm, termed ExpT...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	301,311
2303.09041	A Multimodal Data-driven Framework for Anxiety Screening	Early screening for anxiety and appropriate interventions are essential to reduce the incidence of self-harm and suicide in patients. Due to limited medical resources, traditional methods that overly rely on physician expertise and specialized equipment cannot simultaneously meet the needs for high accuracy and model i...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	351,879
1811.00347	How2: A Large-scale Dataset for Multimodal Language Understanding	In this paper, we introduce How2, a multimodal collection of instructional videos with English subtitles and crowdsourced Portuguese translations. We also present integrated sequence-to-sequence baselines for machine translation, automatic speech recognition, spoken language translation, and multimodal summarization. B...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	112,081
2109.14019	Longitudinal Deep Truck: Deep learning and deep reinforcement learning for modeling and control of longitudinal dynamics of heavy duty trucks	Heavy duty truck mechanical configuration is often tailor designed and built for specific truck mission requirements. This renders the precise derivation of analytical dynamical models and controls for these trucks from first principles challenging, tedious, and often requires several theoretical and applied areas of e...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	257,819
2310.15455	UI Layout Generation with LLMs Guided by UI Grammar	The recent advances in Large Language Models (LLMs) have stimulated interest among researchers and industry professionals, particularly in their application to tasks concerning mobile user interfaces (UIs). This position paper investigates the use of LLMs for UI layout generation. Central to our exploration is the intr...	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	402,315
2103.13674	Frame-rate Up-conversion Detection Based on Convolutional Neural Network for Learning Spatiotemporal Features	With the advance in user-friendly and powerful video editing tools, anyone can easily manipulate videos without leaving prominent visual traces. Frame-rate up-conversion (FRUC), a representative temporal-domain operation, increases the motion continuity of videos with a lower frame-rate and is used by malicious counter...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	true	226,573
2305.14333	Automatic Model Selection with Large Language Models for Reasoning	Chain-of-Thought (CoT) and Program-Aided Language Models (PAL) represent two distinct reasoning methods, each with its own strengths. CoT employs natural language, offering flexibility and interpretability, while PAL utilizes programming language, yielding more structured and rigorous logic. We introduce a model select...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	366,978
2108.07502	MV-TON: Memory-based Video Virtual Try-on network	With the development of Generative Adversarial Network, image-based virtual try-on methods have made great progress. However, limited work has explored the task of video-based virtual try-on while it is important in real-world applications. Most existing video-based virtual try-on methods usually require clothing templ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	250,936
2502.09667	k-LLMmeans: Summaries as Centroids for Interpretable and Scalable LLM-Based Text Clustering	We introduce k-LLMmeans, a novel modification of the k-means clustering algorithm that utilizes LLMs to generate textual summaries as cluster centroids, thereby capturing contextual and semantic nuances often lost when relying on purely numerical means of document embeddings. This modification preserves the properties ...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	533,551
1912.05029	Continual egocentric object recognition	We present a framework capable of tackilng the problem of continual object recognition in a setting which resembles that under whichhumans see and learn. This setting has a set of unique characteristics:it assumes an egocentric point-of-view bound to the needs of a singleperson, which implies a relatively low diversity...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	156,991
1711.06459	Fast Recurrent Fully Convolutional Networks for Direct Perception in Autonomous Driving	Deep convolutional neural networks (CNNs) have been shown to perform extremely well at a variety of tasks including subtasks of autonomous driving such as image segmentation and object classification. However, networks designed for these tasks typically require vast quantities of training data and long training periods...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	84,776
2312.12400	New Classes of the Greedy-Applicable Arm Feature Distributions in the Sparse Linear Bandit Problem	We consider the sparse contextual bandit problem where arm feature affects reward through the inner product of sparse parameters. Recent studies have developed sparsity-agnostic algorithms based on the greedy arm selection policy. However, the analysis of these algorithms requires strong assumptions on the arm feature ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	416,934
1406.4973	Racing Multi-Objective Selection Probabilities	In the context of Noisy Multi-Objective Optimization, dealing with uncertainties requires the decision maker to define some preferences about how to handle them, through some statistics (e.g., mean, median) to be used to evaluate the qualities of the solutions, and define the corresponding Pareto set. Approximating the...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	33,991
2502.02982	FedMobileAgent: Training Mobile Agents Using Decentralized Self-Sourced Data from Diverse Users	The advancement of mobile agents has opened new opportunities for automating tasks on mobile devices. Training these agents requires large-scale high-quality data, which is costly using human labor. Given the vast number of mobile phone users worldwide, if automated data collection from them is feasible, the resulting ...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	530,553
1205.1745	Reconfigurable Controller Design For Actuator Faults In A Four-Tank System Benchmark	The purpose of this work is to design a state feedback controller using Parametric Eigenstructure Assignment (PAE) technique that has the capacity to be reconfigured in the case that partial actuator faults occur. The proposed controller is capable of compensating the gain losses in actuators and maintaining the contro...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	15,854
1412.2897	Trading Wireless Information and Power Transfer: Relay Selection to Minimize the Outage Probability	This paper studies the outage probability minimization problem for a multiple relay network with energy harvesting constraints. The relays are hybrid nodes used for simultaneous wireless information and power transfer from the source radio frequency (RF) signals. There is a trade-off associated with the amount of time ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	38,244
1904.10960	The utility of a convolutional neural network for generating a myelin volume index map from rapid simultaneous relaxometry imaging	Background and Purpose: A current algorithm to obtain a synthetic myelin volume fraction map (SyMVF) from rapid simultaneous relaxometry imaging (RSRI) has a potential problem, that it does not incorporate information from surrounding pixels. The purpose of this study was to develop a method that utilizes a convolution...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	128,752
1905.08087	A Regularized Opponent Model with Maximum Entropy Objective	In a single-agent setting, reinforcement learning (RL) tasks can be cast into an inference problem by introducing a binary random variable o, which stands for the "optimality". In this paper, we redefine the binary random variable o in multi-agent setting and formalize multi-agent reinforcement learning (MARL) as proba...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	true	false	false	false	131,398
1403.5919	SRA: Fast Removal of General Multipath for ToF Sensors	A major issue with Time of Flight sensors is the presence of multipath interference. We present Sparse Reflections Analysis (SRA), an algorithm for removing this interference which has two main advantages. First, it allows for very general forms of multipath, including interference with three or more paths, diffuse mul...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	31,774
2007.00001	Conscious Intelligence Requires Lifelong Autonomous Programming For General Purposes	Universal Turing Machines [29, 10, 18] are well known in computer science but they are about manual programming for general purposes. Although human children perform conscious learning (i.e., learning while being conscious) from infancy [24, 23, 14, 4], it is unknown that Universal Turing Machiness can facilitate not o...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	184,974
2408.15241	GenRec: Unifying Video Generation and Recognition with Diffusion Models	Video diffusion models are able to generate high-quality videos by learning strong spatial-temporal priors on large-scale datasets. In this paper, we aim to investigate whether such priors derived from a generative process are suitable for video recognition, and eventually joint optimization of generation and recogniti...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	483,852
1910.04640	E2FM: an encrypted and compressed full-text index for collections of genomic sequences	Next Generation Sequencing (NGS) platforms and, more generally, high-throughput technologies are giving rise to an exponential growth in the size of nucleotide sequence databases. Moreover, many emerging applications of nucleotide datasets -- as those related to personalized medicine -- require the compliance with regu...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	true	148,817
0710.2611	Geometric Analogue of Holographic Reduced Representation	Holographic reduced representations (HRR) are based on superpositions of convolution-bound $n$-tuples, but the $n$-tuples cannot be regarded as vectors since the formalism is basis dependent. This is why HRR cannot be associated with geometric structures. Replacing convolutions by geometric products one arrives at redu...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	782
2110.07722	The Sigma-max System Induced from Randomness & Fuzziness and its Application in Time Series Prediction	This paper managed to induce probability theory (sigma system) and possibility theory (max system) respectively from the clearly-defined randomness and fuzziness, while focusing the question why the key axiom of "maxitivity" is adopted for possibility measure. Such an objective is achieved by following three steps: a) ...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	261,097
2104.12594	MAQ-CaF: A Modular Air Quality Calibration and Forecasting method for cross-sensitive pollutants	The climatic challenges are rising across the globe in general and in worst hit under-developed countries in particular. The need for accurate measurements and forecasting of pollutants with low-cost deployment is more pertinent today than ever before. Low-cost air quality monitoring sensors are prone to erroneous meas...	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	232,267
2405.12821	Talk2Radar: Bridging Natural Language with 4D mmWave Radar for 3D Referring Expression Comprehension	Embodied perception is essential for intelligent vehicles and robots in interactive environmental understanding. However, these advancements primarily focus on vision, with limited attention given to using 3D modeling sensors, restricting a comprehensive understanding of objects in response to prompts containing qualit...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	455,667
2012.06575	Entropy Maximization and Meta Classification for Out-Of-Distribution Detection in Semantic Segmentation	Deep neural networks (DNNs) for the semantic segmentation of images are usually trained to operate on a predefined closed set of object classes. This is in contrast to the "open world" setting where DNNs are envisioned to be deployed to. From a functional safety point of view, the ability to detect so-called "out-of-di...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	211,161
1908.10705	Improving a State-of-the-Art Heuristic for the Minimum Latency Problem with Data Mining	Recently, hybrid metaheuristics have become a trend in operations research. A successful example combines the Greedy Randomized Adaptive Search Procedures (GRASP) and data mining techniques, where frequent patterns found in high-quality solutions can lead to an efficient exploration of the search space, along with a si...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	143,187
2502.06742	Gradient Multi-Normalization for Stateless and Scalable LLM Training	Training large language models (LLMs) typically relies on adaptive optimizers like Adam (Kingma & Ba, 2015) which store additional state information to accelerate convergence but incur significant memory overhead. Recent efforts, such as SWAN (Ma et al., 2024) address this by eliminating the need for optimizer states w...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	532,214
2112.10541	Implicit Neural Representation Learning for Hyperspectral Image Super-Resolution	Hyperspectral image (HSI) super-resolution without additional auxiliary image remains a constant challenge due to its high-dimensional spectral patterns, where learning an effective spatial and spectral representation is a fundamental issue. Recently, Implicit Neural Representations (INRs) are making strides as a novel...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	272,464
2409.03805	Exploratory Visual Analysis for Increasing Data Readiness in Artificial Intelligence Projects	We present experiences and lessons learned from increasing data readiness of heterogeneous data for artificial intelligence projects using visual analysis methods. Increasing the data readiness level involves understanding both the data as well as the context in which it is used, which are challenges well suitable to v...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	486,186
2201.08283	Lead-lag detection and network clustering for multivariate time series with an application to the US equity market	In multivariate time series systems, it has been observed that certain groups of variables partially lead the evolution of the system, while other variables follow this evolution with a time delay; the result is a lead-lag structure amongst the time series variables. In this paper, we propose a method for the detection...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	276,291
2304.08978	Visual-LiDAR Odometry and Mapping with Monocular Scale Correction and Visual Bootstrapping	This paper presents a novel visual-LiDAR odometry and mapping method with low-drift characteristics. The proposed method is based on two popular approaches, ORB-SLAM and A-LOAM, with monocular scale correction and visual-bootstrapped LiDAR poses initialization modifications. The scale corrector calculates the proportio...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	358,891
1602.02788	Revisiting the Sanders-Freiman-Ruzsa Theorem in $\mathbb{F}_p^n$ and its Application to Non-malleable Codes	Non-malleable codes (NMCs) protect sensitive data against degrees of corruption that prohibit error detection, ensuring instead that a corrupted codeword decodes correctly or to something that bears little relation to the original message. The split-state model, in which codewords consist of two blocks, considers adver...	false	false	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	true	51,908
2007.01620	Team voyTECH: User Activity Modeling with Boosting Trees	This paper describes our winning solution for the ECML-PKDD ChAT Discovery Challenge 2020. We show that whether or not a Twitch user has subscribed to a channel can be well predicted by modeling user activity with boosting trees. We introduce the connection between target-encodings and boosting trees in the context of ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	185,490
2409.09615	Enhancing Text Annotation through Rationale-Driven Collaborative Few-Shot Prompting	The traditional data annotation process is often labor-intensive, time-consuming, and susceptible to human bias, which complicates the management of increasingly complex datasets. This study explores the potential of large language models (LLMs) as automated data annotators to improve efficiency and consistency in anno...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	488,397
2408.05442	Chain of Condition: Construct, Verify and Solve Conditions for Conditional Question Answering	Conditional question answering (CQA) is an important task that aims to find probable answers and identify missing conditions. Existing approaches struggle with CQA due to two challenges: (1) precisely identifying necessary conditions and the logical relationship, and (2) verifying conditions to detect any that are miss...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	479,789
1706.09278	Learning Knowledge Graph Embeddings with Type Regularizer	Learning relations based on evidence from knowledge bases relies on processing the available relation instances. Many relations, however, have clear domain and range, which we hypothesize could help learn a better, more generalizing, model. We include such information in the RESCAL model in the form of a regularization...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	76,112
2003.09491	Intelligent multiscale simulation based on process-guided composite database	In the paper, we present an integrated data-driven modeling framework based on process modeling, material homogenization, mechanistic machine learning, and concurrent multiscale simulation. We are interested in the injection-molded short fiber reinforced composites, which have been identified as key material systems in...	false	true	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	169,061
2405.18253	Proper Dataset Valuation by Pointwise Mutual Information	Data plays a central role in the development of modern artificial intelligence, with high-quality data emerging as a key driver of model performance. This has prompted the development of various data curation methods in recent years. However, measuring the effectiveness of these data curation techniques remains a major...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	458,340
1812.07516	User-Centric Joint Access-Backhaul Design for Full-Duplex Self-Backhauled Wireless Networks	Full-duplex self-backhauling is promising to provide cost-effective and flexible backhaul connectivity for ultra-dense wireless networks, but also poses a great challenge to resource management between the access and backhaul links. In this paper, we propose a user-centric joint access-backhaul transmission framework f...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	116,823
2012.15355	Optimizing Deeper Transformers on Small Datasets	It is a common belief that training deep transformers from scratch requires large datasets. Consequently, for small datasets, people usually use shallow and simple additional layers on top of pre-trained models during fine-tuning. This work shows that this does not always need to be the case: with proper initialization...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	213,753
1801.09063	Capacity Theorems for Distributed Index Coding	In index coding, a server broadcasts multiple messages to their respective receivers, each with some side information that can be utilized to reduce the amount of communication from the server. Distributed index coding is an extension of index coding in which the messages are broadcast from multiple servers, each stori...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	89,042
2502.08866	BrainWavLM: Fine-tuning Speech Representations with Brain Responses to Language	Speech encoding models use auditory representations to predict how the human brain responds to spoken language stimuli. Most performant encoding models linearly map the hidden states of artificial neural networks to brain data, but this linear restriction may limit their effectiveness. In this work, we use low-rank ada...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	533,198
0906.4326	A Logical Characterization of Iterated Admissibility	Brandenburger, Friedenberg, and Keisler provide an epistemic characterization of iterated admissibility (i.e., iterated deletion of weakly dominated strategies) where uncertainty is represented using LPSs (lexicographic probability sequences). Their characterization holds in a rich structure called a complete structure...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	3,952
2005.10851	Conditionally Deep Hybrid Neural Networks Across Edge and Cloud	The pervasiveness of "Internet-of-Things" in our daily life has led to a recent surge in fog computing, encompassing a collaboration of cloud computing and edge intelligence. To that effect, deep learning has been a major driving force towards enabling such intelligent systems. However, growing model sizes in deep lear...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	true	false	false	178,298
2201.12265	3D-FlowNet: Event-based optical flow estimation with 3D representation	Event-based cameras can overpass frame-based cameras limitations for important tasks such as high-speed motion detection during self-driving cars navigation in low illumination conditions. The event cameras' high temporal resolution and high dynamic range, allow them to work in fast motion and extreme light scenarios. ...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	277,580
2308.12833	Use of LLMs for Illicit Purposes: Threats, Prevention Measures, and Vulnerabilities	Spurred by the recent rapid increase in the development and distribution of large language models (LLMs) across industry and academia, much recent work has drawn attention to safety- and security-related threats and vulnerabilities of LLMs, including in the context of potentially criminal activities. Specifically, it h...	false	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	387,685
1207.4161	Identifying Conditional Causal Effects	This paper concerns the assessment of the effects of actions from a combination of nonexperimental data and causal assumptions encoded in the form of a directed acyclic graph in which some variables are presumed to be unobserved. We provide a procedure that systematically identifies cause effects between two sets of va...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	17,585
2410.09344	DARE the Extreme: Revisiting Delta-Parameter Pruning For Fine-Tuned Models	Storing open-source fine-tuned models separately introduces redundancy and increases response times in applications utilizing multiple models. Delta-parameter pruning (DPP), particularly the random drop and rescale (DARE) method proposed by Yu et al., addresses this by pruning the majority of delta parameters--the diff...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	497,548
1807.05597	Deep Learning for Semantic Segmentation on Minimal Hardware	Deep learning has revolutionised many fields, but it is still challenging to transfer its success to small mobile robots with minimal hardware. Specifically, some work has been done to this effect in the RoboCup humanoid football domain, but results that are performant and efficient and still generally applicable outsi...	false	false	false	false	false	false	true	true	false	false	false	true	false	false	false	false	false	false	102,956
1612.08777	Automated timetabling for small colleges and high schools using huge integer programs	We formulate an integer program to solve a highly constrained academic timetabling problem at the United States Merchant Marine Academy. The IP instance that results from our real case study has approximately both 170,000 rows and columns and solves to optimality in 4--24 hours using a commercial solver on a portable c...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	66,108
2408.07834	Assistive Soft Robotic Glove with Ruffles Enhanced Textile Actuators	Hand-wearable robots, specifically exoskeletons, are designed to aid hands in daily activities, playing a crucial role in post-stroke rehabilitation and assisting the elderly. Our contribution to this field is a textile robotic glove with integrated actuators. These actuators, powered by pneumatic pressure, guide the u...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	480,736
2201.05451	A causal model of safety assurance for machine learning	This paper proposes a framework based on a causal model of safety upon which effective safety assurance cases for ML-based applications can be built. In doing so, we build upon established principles of safety engineering as well as previous work on structuring assurance arguments for ML. The paper defines four categor...	false	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	true	275,388
1908.01460	Downlink Analysis of NOMA-enabled Cellular Networks with 3GPP-inspired User Ranking	This paper provides a comprehensive downlink analysis of non-orthogonal multiple access (NOMA) enabled cellular networks using tools from stochastic geometry. As a part of this analysis, we develop a novel 3GPP-inspired user ranking technique to construct a user cluster for the non-orthogonal transmission by grouping u...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	140,771
2306.06093	HyP-NeRF: Learning Improved NeRF Priors using a HyperNetwork	Neural Radiance Fields (NeRF) have become an increasingly popular representation to capture high-quality appearance and shape of scenes and objects. However, learning generalizable NeRF priors over categories of scenes or objects has been challenging due to the high dimensionality of network weight space. To address th...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	372,448
2310.02977	T$^3$Bench: Benchmarking Current Progress in Text-to-3D Generation	Recent methods in text-to-3D leverage powerful pretrained diffusion models to optimize NeRF. Notably, these methods are able to produce high-quality 3D scenes without training on 3D data. Due to the open-ended nature of the task, most studies evaluate their results with subjective case studies and user experiments, the...	false	false	false	false	false	false	true	false	true	false	false	true	false	false	false	false	false	false	397,071
2404.03587	Anticipate & Collab: Data-driven Task Anticipation and Knowledge-driven Planning for Human-robot Collaboration	An agent assisting humans in daily living activities can collaborate more effectively by anticipating upcoming tasks. Data-driven methods represent the state of the art in task anticipation, planning, and related problems, but these methods are resource-hungry and opaque. Our prior work introduced a proof of concept fr...	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	444,316
2209.09233	Learning to Walk by Steering: Perceptive Quadrupedal Locomotion in Dynamic Environments	We tackle the problem of perceptive locomotion in dynamic environments. In this problem, a quadrupedal robot must exhibit robust and agile walking behaviors in response to environmental clutter and moving obstacles. We present a hierarchical learning framework, named PRELUDE, which decomposes the problem of perceptive ...	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	318,439
2501.15941	SAPPHIRE: Preconditioned Stochastic Variance Reduction for Faster Large-Scale Statistical Learning	Regularized empirical risk minimization (rERM) has become important in data-intensive fields such as genomics and advertising, with stochastic gradient methods typically used to solve the largest problems. However, ill-conditioned objectives and non-smooth regularizers undermine the performance of traditional stochasti...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	527,767
2302.09365	Hyneter: Hybrid Network Transformer for Object Detection	In this paper, we point out that the essential differences between CNN-based and Transformer-based detectors, which cause the worse performance of small objects in Transformer-based methods, are the gap between local information and global dependencies in feature extraction and propagation. To address these differences...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	346,398
1212.1224	Random load fluctuations and collapse probability of a power system operating near codimension 1 saddle-node bifurcation	For a power system operating in the vicinity of the power transfer limit of its transmission system, effect of stochastic fluctuations of power loads can become critical as a sufficiently strong such fluctuation may activate voltage instability and lead to a large scale collapse of the system. Considering the effect of...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	20,159
2312.13385	ORBSLAM3-Enhanced Autonomous Toy Drones: Pioneering Indoor Exploration	Navigating toy drones through uncharted GPS-denied indoor spaces poses significant difficulties due to their reliance on GPS for location determination. In such circumstances, the necessity for achieving proper navigation is a primary concern. In response to this formidable challenge, we introduce a real-time autonomou...	false	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	417,291
1511.04854	A Survey of Heterogeneous Information Network Analysis	Most real systems consist of a large number of interacting, multi-typed components, while most contemporary researches model them as homogeneous networks, without distinguishing different types of objects and links in the networks. Recently, more and more researchers begin to consider these interconnected, multi-typed ...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	48,958
1304.2618	Lexicographic identifying codes	An identifying code in a graph is a set of vertices which intersects all the symmetric differences between pairs of neighbourhoods of vertices. Not all graphs have identifying codes; those that do are referred to as twin-free. In this paper, we design an algorithm that finds an identifying code in a twin-free graph on ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	23,712
2205.15582	An Effective Fusion Method to Enhance the Robustness of CNN	With the development of technology rapidly, applications of convolutional neural networks have improved the convenience of our life. However, in image classification field, it has been found that when some perturbations are added to images, the CNN would misclassify it. Thus various defense methods have been proposed. ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	299,790
2311.09325	Temperature-scaling surprisal estimates improve fit to human reading times -- but does it do so for the "right reasons"?	A wide body of evidence shows that human language processing difficulty is predicted by the information-theoretic measure surprisal, a word's negative log probability in context. However, it is still unclear how to best estimate these probabilities needed for predicting human processing difficulty -- while a long-stand...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	408,091
2406.09398	Real-Time Deepfake Detection in the Real-World	Recent improvements in generative AI made synthesizing fake images easy; as they can be used to cause harm, it is crucial to develop accurate techniques to identify them. This paper introduces "Locally Aware Deepfake Detection Algorithm" (LaDeDa), that accepts a single 9x9 image patch and outputs its deepfake score. Th...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	463,917
2408.15621	Convergent Differential Privacy Analysis for General Federated Learning: the $f$-DP Perspective	Federated learning (FL) is an efficient collaborative training paradigm extensively developed with a focus on local privacy, and differential privacy (DP) is a classical approach to capture and ensure the reliability of private security. Their powerful cooperation provides a promising paradigm for the large-scale priva...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	484,009
2405.08609	Dynamic NeRF: A Review	Neural Radiance Field(NeRF) is an novel implicit method to achieve the 3D reconstruction and representation with a high resolution. After the first research of NeRF is proposed, NeRF has gained a robust developing power and is booming in the 3D modeling, representation and reconstruction areas. However the first and mo...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	454,157
2310.05022	Fully Spiking Neural Network for Legged Robots	Recent advancements in legged robots using deep reinforcement learning have led to significant progress. Quadruped robots can perform complex tasks in challenging environments, while bipedal and humanoid robots have also achieved breakthroughs. Current reinforcement learning methods leverage diverse robot bodies and hi...	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	397,942
2111.03997	The Three-Dimensional Structural Configuration of the Central Retinal Vessel Trunk and Branches as a Glaucoma Biomarker	Purpose: To assess whether the three-dimensional (3D) structural configuration of the central retinal vessel trunk and its branches (CRVT&B) could be used as a diagnostic marker for glaucoma. Method: We trained a deep learning network to automatically segment the CRVT&B from the B-scans of the optical coherence tomogra...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	265,343
2201.09071	Towards Sustainable Deep Learning for Wireless Fingerprinting Localization	Location based services, already popular with end users, are now inevitably becoming part of new wireless infrastructures and emerging business processes. The increasingly popular Deep Learning (DL) artificial intelligence methods perform very well in wireless fingerprinting localization based on extensive indoor radio...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	276,544
2004.10165	4D Spatio-Temporal Deep Learning with 4D fMRI Data for Autism Spectrum Disorder Classification	Autism spectrum disorder (ASD) is associated with behavioral and communication problems. Often, functional magnetic resonance imaging (fMRI) is used to detect and characterize brain changes related to the disorder. Recently, machine learning methods have been employed to reveal new patterns by trying to classify ASD fr...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	173,561
1909.09313	Infusing Learned Priors into Model-Based Multispectral Imaging	We introduce a new algorithm for regularized reconstruction of multispectral (MS) images from noisy linear measurements. Unlike traditional approaches, the proposed algorithm regularizes the recovery problem by using a prior specified \emph{only} through a learned denoising function. More specifically, we propose a new...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	146,226
1402.5497	Efficient Semidefinite Spectral Clustering via Lagrange Duality	We propose an efficient approach to semidefinite spectral clustering (SSC), which addresses the Frobenius normalization with the positive semidefinite (p.s.d.) constraint for spectral clustering. Compared with the original Frobenius norm approximation based algorithm, the proposed algorithm can more accurately find the...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	31,070
1911.09785	ReMixMatch: Semi-Supervised Learning with Distribution Alignment and Augmentation Anchoring	We improve the recently-proposed "MixMatch" semi-supervised learning algorithm by introducing two new techniques: distribution alignment and augmentation anchoring. Distribution alignment encourages the marginal distribution of predictions on unlabeled data to be close to the marginal distribution of ground-truth label...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	154,611
2208.10765	A Low-Cost Lane-Following Algorithm for Cyber-Physical Robots	Duckiebots are low-cost mobile robots that are widely used in the fields of research and education. Although there are existing self-driving algorithms for the Duckietown platform, they are either too complex or perform too poorly to navigate a multi-lane track. Moreover, it is essential to give memory and computationa...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	314,193
1909.03362	Assessing Disaster Impacts on Highways Using Social Media: Case Study of Hurricane Harvey	During and after disasters, highways provide vital routes for emergency services, relief efforts, and evacuation activities. Thus, a timely and reliable assessment of disaster impacts on highways is critical for decision-makers to quickly and effectively perform relief and recovery efforts. Recently, social media has i...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	144,451
2201.10164	Investigating the impact of free energy based behavior on human in human-agent interaction	Humans communicate non-verbally by sharing physical rhythms, such as nodding and gestures, to involve each other. This sharing of physicality creates a sense of unity and makes humans feel involved with others. In this paper, we developed a new body motion generation system based on the free-energy principle (FEP), whi...	true	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	276,899
2502.01959	MATCNN: Infrared and Visible Image Fusion Method Based on Multi-scale CNN with Attention Transformer	While attention-based approaches have shown considerable progress in enhancing image fusion and addressing the challenges posed by long-range feature dependencies, their efficacy in capturing local features is compromised by the lack of diverse receptive field extraction techniques. To overcome the shortcomings of exis...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	530,109
2211.14114	Interval-censored Transformer Hawkes: Detecting Information Operations using the Reaction of Social Systems	Social media is being increasingly weaponized by state-backed actors to elicit reactions, push narratives and sway public opinion. These are known as Information Operations (IO). The covert nature of IO makes their detection difficult. This is further amplified by missing data due to the user and content removal and pr...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	332,726
1602.07009	Data-Driven Real-Time Power Dispatch for Maximizing Variable Renewable Generation	Traditional power dispatch methods have difficulties in accommodating large-scale variable renewable generation (VRG) and have resulted in unnecessary VRG spillage in the practical industry. The recent dispatchable-interval-based methods have the potential to reduce VRG curtailment, but the dispatchable intervals are n...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	52,451
2204.10983	Federated Contrastive Learning for Volumetric Medical Image Segmentation	Supervised deep learning needs a large amount of labeled data to achieve high performance. However, in medical imaging analysis, each site may only have a limited amount of data and labels, which makes learning ineffective. Federated learning (FL) can help in this regard by learning a shared model while keeping trainin...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	292,988
2012.14791	Drift-Aware Multi-Memory Model for Imbalanced Data Streams	Online class imbalance learning deals with data streams that are affected by both concept drift and class imbalance. Online learning tries to find a trade-off between exploiting previously learned information and incorporating new information into the model. This requires both the incremental update of the model and th...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	213,610
2302.09605	Efficient Communication via Self-supervised Information Aggregation for Online and Offline Multi-agent Reinforcement Learning	Utilizing messages from teammates can improve coordination in cooperative Multi-agent Reinforcement Learning (MARL). Previous works typically combine raw messages of teammates with local information as inputs for policy. However, neglecting message aggregation poses significant inefficiency for policy learning. Motivat...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	false	false	false	346,494
2412.07051	A Misclassification Network-Based Method for Comparative Genomic Analysis	Classifying genome sequences based on metadata has been an active area of research in comparative genomics for decades with many important applications across the life sciences. Established methods for classifying genomes can be broadly grouped into sequence alignment-based and alignment-free models. Conventional align...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	515,487
2408.00542	Secret Sharing for Secure and Private Information Retrieval: A Construction Using Algebraic Geometry Codes	Private information retrieval (PIR) considers the problem of retrieving a data item from a database or distributed storage system without disclosing any information about which data item was retrieved. Secure PIR complements this problem by further requiring the contents of the data to be kept secure. Privacy and secur...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	477,881
2006.02001	Learning with CVaR-based feedback under potentially heavy tails	We study learning algorithms that seek to minimize the conditional value-at-risk (CVaR), when all the learner knows is that the losses incurred may be heavy-tailed. We begin by studying a general-purpose estimator of CVaR for potentially heavy-tailed random variables, which is easy to implement in practice, and require...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	179,929
2108.06552	Continual Semi-Supervised Learning through Contrastive Interpolation Consistency	Continual Learning (CL) investigates how to train Deep Networks on a stream of tasks without incurring forgetting. CL settings proposed in literature assume that every incoming example is paired with ground-truth annotations. However, this clashes with many real-world applications: gathering labeled data, which is in i...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	250,633
2212.11677	DuAT: Dual-Aggregation Transformer Network for Medical Image Segmentation	Transformer-based models have been widely demonstrated to be successful in computer vision tasks by modelling long-range dependencies and capturing global representations. However, they are often dominated by features of large patterns leading to the loss of local details (e.g., boundaries and small objects), which are...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	337,848
2404.00846	Transfer Learning with Point Transformers	Point Transformers are near state-of-the-art models for classification, segmentation, and detection tasks on Point Cloud data. They utilize a self attention based mechanism to model large range spatial dependencies between multiple point sets. In this project we explore two things: classification performance of these a...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	443,125
1911.11852	Rule Designs for Optimal Online Game Matchmaking	Online games are the most popular form of entertainment among youngsters as well as elders. Recognized as e-Sports, they may become an official part of the Olympic Games by 2020. However, a long waiting time for matchmaking will largely affect players' experiences. We examine different matchmaking mechanisms for 2v2 ga...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	true	155,236

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