Split (2)

train · 433k rows

id stringlengths 9 16	title stringlengths 4 278	abstract stringlengths 3 4.08k	cs.HC bool 2 classes	cs.CE bool 2 classes	cs.SD bool 2 classes	cs.SI bool 2 classes	cs.AI bool 2 classes	cs.IR bool 2 classes	cs.LG bool 2 classes	cs.RO bool 2 classes	cs.CL bool 2 classes	cs.IT bool 2 classes	cs.SY bool 2 classes	cs.CV bool 2 classes	cs.CR bool 2 classes	cs.CY bool 2 classes	cs.MA bool 2 classes	cs.NE bool 2 classes	cs.DB bool 2 classes	Other bool 2 classes	__index_level_0__ int64 0 541k
2305.16681	CAILA: Concept-Aware Intra-Layer Adapters for Compositional Zero-Shot Learning	In this paper, we study the problem of Compositional Zero-Shot Learning (CZSL), which is to recognize novel attribute-object combinations with pre-existing concepts. Recent researchers focus on applying large-scale Vision-Language Pre-trained (VLP) models like CLIP with strong generalization ability. However, these met...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	368,205
2408.08248	Conformalized Answer Set Prediction for Knowledge Graph Embedding	Knowledge graph embeddings (KGE) apply machine learning methods on knowledge graphs (KGs) to provide non-classical reasoning capabilities based on similarities and analogies. The learned KG embeddings are typically used to answer queries by ranking all potential answers, but rankings often lack a meaningful probabilist...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	480,921
2206.03834	Boosting the Confidence of Generalization for $L_2$-Stable Randomized Learning Algorithms	Exponential generalization bounds with near-tight rates have recently been established for uniformly stable learning algorithms. The notion of uniform stability, however, is stringent in the sense that it is invariant to the data-generating distribution. Under the weaker and distribution dependent notions of stability ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	301,423
1612.01114	On the Performance of Visible Light Communications Systems with Non-Orthogonal Multiple Access	Visible light communications (VLC) have been recently proposed as a promising and efficient solution to indoor ubiquitous broadband connectivity. In this paper, non-orthogonal multiple access, which has been recently proposed as an effective scheme for fifth generation (5G) wireless networks, is considered in the conte...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	65,020
1511.04401	Symbol Grounding Association in Multimodal Sequences with Missing Elements	In this paper, we extend a symbolic association framework for being able to handle missing elements in multimodal sequences. The general scope of the work is the symbolic associations of object-word mappings as it happens in language development in infants. In other words, two different representations of the same abst...	false	false	false	false	false	false	true	false	true	false	false	true	false	false	false	true	false	false	48,886
2305.08283	From Pretraining Data to Language Models to Downstream Tasks: Tracking the Trails of Political Biases Leading to Unfair NLP Models	Language models (LMs) are pretrained on diverse data sources, including news, discussion forums, books, and online encyclopedias. A significant portion of this data includes opinions and perspectives which, on one hand, celebrate democracy and diversity of ideas, and on the other hand are inherently socially biased. Ou...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	364,226
2201.11876	Regionalized Optimization	We propose a theoretical framework for non redundant reconstruction of a global loss from a collection of local ones under constraints given by a functor; we call this loss the regionalized loss in honor to Yedidia, Freeman, Weiss' celebrated article `Constructing free-energy approximations and generalized belief propa...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	277,433
2009.14825	Deep Reinforcement Learning for Efficient Measurement of Quantum Devices	Deep reinforcement learning is an emerging machine learning approach which can teach a computer to learn from their actions and rewards similar to the way humans learn from experience. It offers many advantages in automating decision processes to navigate large parameter spaces. This paper proposes a novel approach to ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	198,155
2309.16158	FireFly v2: Advancing Hardware Support for High-Performance Spiking Neural Network with a Spatiotemporal FPGA Accelerator	Spiking Neural Networks (SNNs) are expected to be a promising alternative to Artificial Neural Networks (ANNs) due to their strong biological interpretability and high energy efficiency. Specialized SNN hardware offers clear advantages over general-purpose devices in terms of power and performance. However, there's sti...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	true	395,242
1811.12008	Efficient Semantic Segmentation for Visual Bird's-eye View Interpretation	The ability to perform semantic segmentation in real-time capable applications with limited hardware is of great importance. One such application is the interpretation of the visual bird's-eye view, which requires the semantic segmentation of the four omnidirectional camera images. In this paper, we present an efficien...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	114,911
1805.01199	Label Embedding with Partial Heterogeneous Contexts	Label embedding plays an important role in many real-world applications. To enhance the label relatedness captured by the embeddings, multiple contexts can be adopted. However, these contexts are heterogeneous and often partially observed in practical tasks, imposing significant challenges to capture the overall relate...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	96,608
1711.01371	An Iterative Co-Saliency Framework for RGBD Images	As a newly emerging and significant topic in computer vision community, co-saliency detection aims at discovering the common salient objects in multiple related images. The existing methods often generate the co-saliency map through a direct forward pipeline which is based on the designed cues or initialization, but la...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	83,870
2405.03692	Imitation Learning for Adaptive Video Streaming with Future Adversarial Information Bottleneck Principle	Adaptive video streaming plays a crucial role in ensuring high-quality video streaming services. Despite extensive research efforts devoted to Adaptive BitRate (ABR) techniques, the current reinforcement learning (RL)-based ABR algorithms may benefit the average Quality of Experience (QoE) but suffers from fluctuating ...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	true	452,279
2412.19535	StyleRWKV: High-Quality and High-Efficiency Style Transfer with RWKV-like Architecture	Style transfer aims to generate a new image preserving the content but with the artistic representation of the style source. Most of the existing methods are based on Transformers or diffusion models, however, they suffer from quadratic computational complexity and high inference time. RWKV, as an emerging deep sequenc...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	520,889
2311.11955	Multi-Agent Strategy Explanations for Human-Robot Collaboration	As robots are deployed in human spaces, it is important that they are able to coordinate their actions with the people around them. Part of such coordination involves ensuring that people have a good understanding of how a robot will act in the environment. This can be achieved through explanations of the robot's polic...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	409,141
2204.08121	End-to-end Dense Video Captioning as Sequence Generation	Dense video captioning aims to identify the events of interest in an input video, and generate descriptive captions for each event. Previous approaches usually follow a two-stage generative process, which first proposes a segment for each event, then renders a caption for each identified segment. Recent advances in lar...	false	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	291,969
2008.00247	Meta-DRN: Meta-Learning for 1-Shot Image Segmentation	Modern deep learning models have revolutionized the field of computer vision. But, a significant drawback of most of these models is that they require a large number of labelled examples to generalize properly. Recent developments in few-shot learning aim to alleviate this requirement. In this paper, we propose a novel...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	189,954
0902.4881	Controllability and observabiliy of an artificial advection-diffusion problem	In this paper we study the controllability of an artificial advection-diffusion system through the boundary. Suitable Carleman estimates give us the observability on the adjoint system in the one dimensional case. We also study some basic properties of our problem such as backward uniqueness and we get an intuitive res...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	3,246
1208.4790	Worst-Case Expected-Capacity Loss of Slow-Fading Channels	For delay-limited communication over block-fading channels, the difference between the ergodic capacity and the maximum achievable expected rate for coding over a finite number of coherent blocks represents a fundamental measure of the penalty incurred by the delay constraint. This paper introduces a notion of worst-ca...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	18,231
2208.11099	Explaining Bias in Deep Face Recognition via Image Characteristics	In this paper, we propose a novel explanatory framework aimed to provide a better understanding of how face recognition models perform as the underlying data characteristics (protected attributes: gender, ethnicity, age; non-protected attributes: facial hair, makeup, accessories, face orientation and occlusion, image d...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	314,313
2008.03209	Investigating maximum likelihood based training of infinite mixtures for uncertainty quantification	Uncertainty quantification in neural networks gained a lot of attention in the past years. The most popular approaches, Bayesian neural networks (BNNs), Monte Carlo dropout, and deep ensembles have one thing in common: they are all based on some kind of mixture model. While the BNNs build infinite mixture models and ar...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	190,835
2202.06248	A Tech Hybrid-Recommendation Engine and Personalized Notification: An integrated tool to assist users through Recommendations (Project ATHENA)	Project ATHENA aims to develop an application to address information overload, primarily focused on Recommendation Systems (RSs) with the personalization and user experience design of a modern system. Two machine learning (ML) algorithms were used: (1) TF-IDF for Content-based filtering (CBF); (2) Classification with M...	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	280,153
2201.12975	Rotting Infinitely Many-armed Bandits	We consider the infinitely many-armed bandit problem with rotting rewards, where the mean reward of an arm decreases at each pull of the arm according to an arbitrary trend with maximum rotting rate $\varrho=o(1)$. We show that this learning problem has an $\Omega(\max\{\varrho^{1/3}T,\sqrt{T}\})$ worst-case regret low...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	277,851
1812.10437	Structure Learning of Sparse GGMs over Multiple Access Networks	A central machine is interested in estimating the underlying structure of a sparse Gaussian Graphical Model (GGM) from datasets distributed across multiple local machines. The local machines can communicate with the central machine through a wireless multiple access channel. In this paper, we are interested in designin...	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	true	117,367
2501.01238	EHCTNet: Enhanced Hybrid of CNN and Transformer Network for Remote Sensing Image Change Detection	Remote sensing (RS) change detection incurs a high cost because of false negatives, which are more costly than false positives. Existing frameworks, struggling to improve the Precision metric to reduce the cost of false positive, still have limitations in focusing on the change of interest, which leads to missed detect...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	521,986
2106.12320	BiblioDAP: The 1st Workshop on Bibliographic Data Analysis and Processing	Automatic processing of bibliographic data becomes very important in digital libraries, data science and machine learning due to its importance in keeping pace with the significant increase of published papers every year from one side and to the inherent challenges from the other side. This processing has several aspec...	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	true	242,696
1707.05471	DCTM: Discrete-Continuous Transformation Matching for Semantic Flow	Techniques for dense semantic correspondence have provided limited ability to deal with the geometric variations that commonly exist between semantically similar images. While variations due to scale and rotation have been examined, there lack practical solutions for more complex deformations such as affine transformat...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	77,240
2306.09247	ATLAS: Automatically Detecting Discrepancies Between Privacy Policies and Privacy Labels	Privacy policies are long, complex documents that end-users seldom read. Privacy labels aim to ameliorate these issues by providing succinct summaries of salient data practices. In December 2020, Apple began requiring that app developers submit privacy labels describing their apps' data practices. Yet, research suggest...	false	false	false	false	true	false	true	false	false	false	false	false	true	false	false	false	false	false	373,731
2310.11875	Fractional Concepts in Neural Networks: Enhancing Activation Functions	Designing effective neural networks requires tuning architectural elements. This study integrates fractional calculus into neural networks by introducing fractional order derivatives (FDO) as tunable parameters in activation functions, allowing diverse activation functions by adjusting the FDO. We evaluate these fracti...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	400,819
1702.04941	Station-keeping control of an unmanned surface vehicle exposed to current and wind disturbances	Field trials of a 4 meter long, 180 kilogram, unmanned surface vehicle (USV) have been conducted to evaluate the performance of station-keeping heading and position controllers in an outdoor marine environment disturbed by wind and current. The USV has a twin hull configuration and a custom-designed propulsion system, ...	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	68,334
1706.05476	An Efficient Probabilistic Approach for Graph Similarity Search	Graph similarity search is a common and fundamental operation in graph databases. One of the most popular graph similarity measures is the Graph Edit Distance (GED) mainly because of its broad applicability and high interpretability. Despite its prevalence, exact GED computation is proved to be NP-hard, which could res...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	true	75,520
2405.06134	Muting Whisper: A Universal Acoustic Adversarial Attack on Speech Foundation Models	Recent developments in large speech foundation models like Whisper have led to their widespread use in many automatic speech recognition (ASR) applications. These systems incorporate `special tokens' in their vocabulary, such as $\texttt{<\|endoftext\|>}$, to guide their language generation process. However, we demonstra...	false	false	true	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	453,182
2001.08603	Learning Distributional Programs for Relational Autocompletion	Relational autocompletion is the problem of automatically filling out some missing values in multi-relational data. We tackle this problem within the probabilistic logic programming framework of Distributional Clauses (DC), which supports both discrete and continuous probability distributions. Within this framework, we...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	true	161,333
1805.02850	Joint Cell Nuclei Detection and Segmentation in Microscopy Images Using 3D Convolutional Networks	We propose a 3D convolutional neural network to simultaneously segment and detect cell nuclei in confocal microscopy images. Mirroring the co-dependency of these tasks, our proposed model consists of two serial components: the first part computes a segmentation of cell bodies, while the second module identifies the cen...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	96,931
2405.10202	Hierarchical Attention Graph for Scientific Document Summarization in Global and Local Level	Scientific document summarization has been a challenging task due to the long structure of the input text. The long input hinders the simultaneous effective modeling of both global high-order relations between sentences and local intra-sentence relations which is the most critical step in extractive summarization. Howe...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	454,673
2311.05019	DEMASQ: Unmasking the ChatGPT Wordsmith	The potential misuse of ChatGPT and other Large Language Models (LLMs) has raised concerns regarding the dissemination of false information, plagiarism, academic dishonesty, and fraudulent activities. Consequently, distinguishing between AI-generated and human-generated content has emerged as an intriguing research top...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	406,439
2409.08130	The JPEG Pleno Learning-based Point Cloud Coding Standard: Serving Man and Machine	Efficient point cloud coding has become increasingly critical for multiple applications such as virtual reality, autonomous driving, and digital twin systems, where rich and interactive 3D data representations may functionally make the difference. Deep learning has emerged as a powerful tool in this domain, offering ad...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	487,780
2302.08434	On marginal feature attributions of tree-based models	Due to their power and ease of use, tree-based machine learning models, such as random forests and gradient-boosted tree ensembles, have become very popular. To interpret them, local feature attributions based on marginal expectations, e.g. marginal (interventional) Shapley, Owen or Banzhaf values, may be employed. Suc...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	346,048
2003.09361	Traffic Abstractions of Nonlinear Homogeneous Event-Triggered Control Systems	In previous work, linear time-invariant event-triggered control (ETC) systems were abstracted to finite-state systems that capture the original systems' sampling behaviour. It was shown that these abstractions can be employed for scheduling of communication traffic in networks of ETC loops. In this paper, we extend thi...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	169,024
2203.10659	From Stance to Concern: Adaptation of Propositional Analysis to New Tasks and Domains	We present a generalized paradigm for adaptation of propositional analysis (predicate-argument pairs) to new tasks and domains. We leverage an analogy between stances (belief-driven sentiment) and concerns (topical issues with moral dimensions/endorsements) to produce an explanatory representation. A key contribution i...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	286,632
2402.15351	AutoMMLab: Automatically Generating Deployable Models from Language Instructions for Computer Vision Tasks	Automated machine learning (AutoML) is a collection of techniques designed to automate the machine learning development process. While traditional AutoML approaches have been successfully applied in several critical steps of model development (e.g. hyperparameter optimization), there lacks a AutoML system that automate...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	432,108
1206.5281	Learning Selectively Conditioned Forest Structures with Applications to DBNs and Classification	Dealing with uncertainty in Bayesian Network structures using maximum a posteriori (MAP) estimation or Bayesian Model Averaging (BMA) is often intractable due to the superexponential number of possible directed, acyclic graphs. When the prior is decomposable, two classes of graphs where efficient learning can take plac...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	16,818
2405.21061	Graph External Attention Enhanced Transformer	The Transformer architecture has recently gained considerable attention in the field of graph representation learning, as it naturally overcomes several limitations of Graph Neural Networks (GNNs) with customized attention mechanisms or positional and structural encodings. Despite making some progress, existing works t...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	459,632
2401.04305	Advancing Deep Active Learning & Data Subset Selection: Unifying Principles with Information-Theory Intuitions	At its core, this thesis aims to enhance the practicality of deep learning by improving the label and training efficiency of deep learning models. To this end, we investigate data subset selection techniques, specifically active learning and active sampling, grounded in information-theoretic principles. Active learning...	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	420,383
1402.2606	A Fast Two Pass Multi-Value Segmentation Algorithm based on Connected Component Analysis	Connected component analysis (CCA) has been heavily used to label binary images and classify segments. However, it has not been well-exploited to segment multi-valued natural images. This work proposes a novel multi-value segmentation algorithm that utilizes CCA to segment color images. A user defined distance measure ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	30,796
2312.04101	Edge computing service deployment and task offloading based on multi-task high-dimensional multi-objective optimization	The Mobile Edge Computing (MEC) system located close to the client allows mobile smart devices to offload their computations onto edge servers, enabling them to benefit from low-latency computing services. Both cloud service providers and users seek more comprehensive solutions, necessitating judicious decisions in ser...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	413,552
1904.04467	Explaining Wrong Queries Using Small Examples	For testing the correctness of SQL queries, e.g., evaluating student submissions in a database course, a standard practice is to execute the query in question on some test database instance and compare its result with that of the correct query. Given two queries $Q_1$ and $Q_2$, we say that a database instance $D$ is a...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	127,048
2408.04705	Overlay-based Decentralized Federated Learning in Bandwidth-limited Networks	The emerging machine learning paradigm of decentralized federated learning (DFL) has the promise of greatly boosting the deployment of artificial intelligence (AI) by directly learning across distributed agents without centralized coordination. Despite significant efforts on improving the communication efficiency of DF...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	479,497
1602.02490	Simulation of bifurcated stent grafts to treat abdominal aortic aneurysms (AAA)	In this paper a method is introduced, to visualize bifurcated stent grafts in CT-Data. The aim is to improve therapy planning for minimal invasive treatment of abdominal aortic aneurysms (AAA). Due to precise measurement of the abdominal aortic aneurysm and exact simulation of the bifurcated stent graft, physicians are...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	51,862
2407.08723	Topological Generalization Bounds for Discrete-Time Stochastic Optimization Algorithms	We present a novel set of rigorous and computationally efficient topology-based complexity notions that exhibit a strong correlation with the generalization gap in modern deep neural networks (DNNs). DNNs show remarkable generalization properties, yet the source of these capabilities remains elusive, defying the establ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	472,284
1412.7680	A Fuzzy Based Model to Identify Printed Sinhala Characters (ICIAfS14)	Character recognition techniques for printed documents are widely used for English language. However, the systems that are implemented to recognize Asian languages struggle to increase the accuracy of recognition. Among other Asian languages (such as Arabic, Tamil, Chinese), Sinhala characters are unique, mainly becaus...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	38,829
2411.11717	RAWMamba: Unified sRGB-to-RAW De-rendering With State Space Model	Recent advancements in sRGB-to-RAW de-rendering have increasingly emphasized metadata-driven approaches to reconstruct RAW data from sRGB images, supplemented by partial RAW information. In image-based de-rendering, metadata is commonly obtained through sampling, whereas in video tasks, it is typically derived from the...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	509,150
2011.04112	Learning Hybrid Control Barrier Functions from Data	Motivated by the lack of systematic tools to obtain safe control laws for hybrid systems, we propose an optimization-based framework for learning certifiably safe control laws from data. In particular, we assume a setting in which the system dynamics are known and in which data exhibiting safe system behavior is availa...	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	205,464
2203.02180	EAG: Extract and Generate Multi-way Aligned Corpus for Complete Multi-lingual Neural Machine Translation	Complete Multi-lingual Neural Machine Translation (C-MNMT) achieves superior performance against the conventional MNMT by constructing multi-way aligned corpus, i.e., aligning bilingual training examples from different language pairs when either their source or target sides are identical. However, since exactly identic...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	283,670
2502.02279	A Revisit of Total Correlation in Disentangled Variational Auto-Encoder with Partial Disentanglement	A fully disentangled variational auto-encoder (VAE) aims to identify disentangled latent components from observations. However, enforcing full independence between all latent components may be too strict for certain datasets. In some cases, multiple factors may be entangled together in a non-separable manner, or a sing...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	530,245
2411.09852	InterFormer: Towards Effective Heterogeneous Interaction Learning for Click-Through Rate Prediction	Click-through rate (CTR) prediction, which predicts the probability of a user clicking an ad, is a fundamental task in recommender systems. The emergence of heterogeneous information, such as user profile and behavior sequences, depicts user interests from different aspects. A mutually beneficial integration of heterog...	false	false	false	false	true	true	true	false	false	false	false	false	false	false	false	false	false	false	508,392
1802.04183	On Index Codes for Interlinked Cycle Structured Side-Information Graphs	In connection with the index code construction and the decoding algorithm for interlinked cycle (IC) structures proposed by Thapa, Ong and Johnson in \cite{TOJ} ("Interlinked Cycles for Index Coding: Generalizing Cycles and Cliques", IEEE Trans. Inf. Theory, vol. 63, no. 6, Jun. 2017), it is shown in \cite{VaR} ("Optim...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	90,162
2003.03284	TaskNorm: Rethinking Batch Normalization for Meta-Learning	Modern meta-learning approaches for image classification rely on increasingly deep networks to achieve state-of-the-art performance, making batch normalization an essential component of meta-learning pipelines. However, the hierarchical nature of the meta-learning setting presents several challenges that can render con...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	167,182
2110.13986	Fair Sequential Selection Using Supervised Learning Models	We consider a selection problem where sequentially arrived applicants apply for a limited number of positions/jobs. At each time step, a decision maker accepts or rejects the given applicant using a pre-trained supervised learning model until all the vacant positions are filled. In this paper, we discuss whether the fa...	false	false	false	false	true	false	true	false	false	false	false	false	false	true	false	false	false	false	263,367
1510.07566	Least costly energy management for series hybrid electric vehicles	Energy management of plug-in Hybrid Electric Vehicles (HEVs) has different challenges from non-plug-in HEVs, due to bigger batteries and grid recharging. Instead of tackling it to pursue energetic efficiency, an approach minimizing the driving cost incurred by the user - the combined costs of fuel, grid energy and batt...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	48,215
2405.01010	Efficient and Adaptive Posterior Sampling Algorithms for Bandits	We study Thompson Sampling-based algorithms for stochastic bandits with bounded rewards. As the existing problem-dependent regret bound for Thompson Sampling with Gaussian priors [Agrawal and Goyal, 2017] is vacuous when $T \le 288 e^{64}$, we derive a more practical bound that tightens the coefficient of the leading t...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	451,180
2410.02547	Personalized Quantum Federated Learning for Privacy Image Classification	Quantum federated learning has brought about the improvement of privacy image classification, while the lack of personality of the client model may contribute to the suboptimal of quantum federated learning. A personalized quantum federated learning algorithm for privacy image classification is proposed to enhance the ...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	494,325
2406.16370	An Active Search Strategy with Multiple Unmanned Aerial Systems for Multiple Targets	The challenge of efficient target searching in vast natural environments has driven the need for advanced multi-UAV active search strategies. This paper introduces a novel method in which global and local information is adeptly merged to avoid issues such as myopia and redundant back-and-forth movements. In addition, a...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	467,108
1206.6486	Flexible Modeling of Latent Task Structures in Multitask Learning	Multitask learning algorithms are typically designed assuming some fixed, a priori known latent structure shared by all the tasks. However, it is usually unclear what type of latent task structure is the most appropriate for a given multitask learning problem. Ideally, the "right" latent task structure should be learne...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	17,021
1904.03310	Gender Bias in Contextualized Word Embeddings	In this paper, we quantify, analyze and mitigate gender bias exhibited in ELMo's contextualized word vectors. First, we conduct several intrinsic analyses and find that (1) training data for ELMo contains significantly more male than female entities, (2) the trained ELMo embeddings systematically encode gender informat...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	126,677
2407.10264	What Makes and Breaks Safety Fine-tuning? A Mechanistic Study	Safety fine-tuning helps align Large Language Models (LLMs) with human preferences for their safe deployment. To better understand the underlying factors that make models safe via safety fine-tuning, we design a synthetic data generation framework that captures salient aspects of an unsafe input by modeling the interac...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	472,909
2305.12171	Diffusion Co-Policy for Synergistic Human-Robot Collaborative Tasks	Modeling multimodal human behavior has been a key barrier to increasing the level of interaction between human and robot, particularly for collaborative tasks. Our key insight is that an effective, learned robot policy used for human-robot collaborative tasks must be able to express a high degree of multimodality, pred...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	365,873
2109.04703	Heterogeneous Graph Neural Networks for Keyphrase Generation	The encoder-decoder framework achieves state-of-the-art results in keyphrase generation (KG) tasks by predicting both present keyphrases that appear in the source document and absent keyphrases that do not. However, relying solely on the source document can result in generating uncontrollable and inaccurate absent keyp...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	254,502
1306.2347	Auditing: Active Learning with Outcome-Dependent Query Costs	We propose a learning setting in which unlabeled data is free, and the cost of a label depends on its value, which is not known in advance. We study binary classification in an extreme case, where the algorithm only pays for negative labels. Our motivation are applications such as fraud detection, in which investigatin...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	25,118
1205.2642	Improved Mean and Variance Approximations for Belief Net Responses via Network Doubling	A Bayesian belief network models a joint distribution with an directed acyclic graph representing dependencies among variables and network parameters characterizing conditional distributions. The parameters are viewed as random variables to quantify uncertainty about their values. Belief nets are used to compute respon...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	15,948
2304.12777	Class Attention Transfer Based Knowledge Distillation	Previous knowledge distillation methods have shown their impressive performance on model compression tasks, however, it is hard to explain how the knowledge they transferred helps to improve the performance of the student network. In this work, we focus on proposing a knowledge distillation method that has both high in...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	360,347
2004.04331	Robust Linear Precoder Design for 3D Massive MIMO Downlink with A Posteriori Channel Model	In this paper, we investigate the robust linear precoder design for three dimensional (3D) massive multi-input multi-output (MIMO) downlink with uniform planar array (UPA) and imperfect channel state information (CSI). In practical massive MIMO with UPAs, the number of antennas in each column or row is usually limited....	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	171,848
1503.05214	Analysis of PCA Algorithms in Distributed Environments	Classical machine learning algorithms often face scalability bottlenecks when they are applied to large-scale data. Such algorithms were designed to work with small data that is assumed to fit in the memory of one machine. In this report, we analyze different methods for computing an important machine learing algorithm...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	41,221
1505.05794	An Improved Upper Bound for the Most Informative Boolean Function Conjecture	Suppose $X$ is a uniformly distributed $n$-dimensional binary vector and $Y$ is obtained by passing $X$ through a binary symmetric channel with crossover probability $\alpha$. A recent conjecture by Courtade and Kumar postulates that $I(f(X);Y)\leq 1-h(\alpha)$ for any Boolean function $f$. So far, the best known upper...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	43,347
2406.01047	An Advanced Reinforcement Learning Framework for Online Scheduling of Deferrable Workloads in Cloud Computing	Efficient resource utilization and perfect user experience usually conflict with each other in cloud computing platforms. Great efforts have been invested in increasing resource utilization but trying not to affect users' experience for cloud computing platforms. In order to better utilize the remaining pieces of compu...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	true	460,143
1912.02522	VoxSRC 2019: The first VoxCeleb Speaker Recognition Challenge	The VoxCeleb Speaker Recognition Challenge 2019 aimed to assess how well current speaker recognition technology is able to identify speakers in unconstrained or `in the wild' data. It consisted of: (i) a publicly available speaker recognition dataset from YouTube videos together with ground truth annotation and standar...	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	156,371
2212.08363	Fast Learning of Dynamic Hand Gesture Recognition with Few-Shot Learning Models	We develop Few-Shot Learning models trained to recognize five or ten different dynamic hand gestures, respectively, which are arbitrarily interchangeable by providing the model with one, two, or five examples per hand gesture. All models were built in the Few-Shot Learning architecture of the Relation Network (RN), in ...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	336,725
2006.01546	Workspace monitoring and planning for safe mobile manipulation	In order to enable physical human-robot interaction where humans and (mobile) manipulators share their workspace and work together, robots have to be equipped with important capabilities to guarantee human safety. The robots have to recognize possible collisions with the human co-worker and react anticipatorily by adap...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	179,807
1204.3890	Collective Creativity: Where we are and where we might go	Creativity is individual, and it is social. The social aspects of creativity have become of increasing interest as systems have emerged that mobilize large numbers of people to engage in creative tasks. We examine research related to collective intelligence and differentiate work on collective creativity from other col...	true	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	15,546
2208.06613	A Study of Demographic Bias in CNN-based Brain MR Segmentation	Convolutional neural networks (CNNs) are increasingly being used to automate the segmentation of brain structures in magnetic resonance (MR) images for research studies. In other applications, CNN models have been shown to exhibit bias against certain demographic groups when they are under-represented in the training s...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	312,771
2103.14969	Catalyzing Clinical Diagnostic Pipelines Through Volumetric Medical Image Segmentation Using Deep Neural Networks: Past, Present, & Future	Deep learning has made a remarkable impact in the field of natural image processing over the past decade. Consequently, there is a great deal of interest in replicating this success across unsolved tasks in related domains, such as medical image analysis. Core to medical image analysis is the task of semantic segmentat...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	227,035
1807.03750	Navigating Diverse Data Science Learning: Critical Reflections Towards Future Practice	Data Science is currently a popular field of science attracting expertise from very diverse backgrounds. Current learning practices need to acknowledge this and adapt to it. This paper summarises some experiences relating to such learning approaches from teaching a postgraduate Data Science module, and draws some learn...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	102,601
2012.14230	Longitudinal diffusion MRI analysis using Segis-Net: a single-step deep-learning framework for simultaneous segmentation and registration	This work presents a single-step deep-learning framework for longitudinal image analysis, coined Segis-Net. To optimally exploit information available in longitudinal data, this method concurrently learns a multi-class segmentation and nonlinear registration. Segmentation and registration are modeled using a convolutio...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	213,446
1701.06504	On Spectral Coexistence of CP-OFDM and FB-MC Waveforms in 5G Networks	Future 5G networks will serve a variety of applications that will coexist on the same spectral band and geographical area, in an uncoordinated and asynchronous manner. It is widely accepted that using CP-OFDM, the waveform used by most current communication systems, will make it difficult to achieve this paradigm. Espe...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	67,148
2206.09236	Model-Agnostic Few-Shot Open-Set Recognition	We tackle the Few-Shot Open-Set Recognition (FSOSR) problem, i.e. classifying instances among a set of classes for which we only have few labeled samples, while simultaneously detecting instances that do not belong to any known class. Departing from existing literature, we focus on developing model-agnostic inference m...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	303,478
2304.01005	Federated Learning Based Multilingual Emoji Prediction In Clean and Attack Scenarios	Federated learning is a growing field in the machine learning community due to its decentralized and private design. Model training in federated learning is distributed over multiple clients giving access to lots of client data while maintaining privacy. Then, a server aggregates the training done on these multiple cli...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	355,899
2208.09951	Individual Fairness under Varied Notions of Group Fairness in Bipartite Matching - One Framework to Approximate Them All	We study the probabilistic assignment of items to platforms that satisfies both group and individual fairness constraints. Each item belongs to specific groups and has a preference ordering over platforms. Each platform enforces group fairness by limiting the number of items per group that can be assigned to it. There ...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	313,897
0805.0330	Alternating Automata on Data Trees and XPath Satisfiability	A data tree is an unranked ordered tree whose every node is labelled by a letter from a finite alphabet and an element ("datum") from an infinite set, where the latter can only be compared for equality. The article considers alternating automata on data trees that can move downward and rightward, and have one register ...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	true	1,706
2303.06832	ODIN: On-demand Data Formulation to Mitigate Dataset Lock-in	ODIN is an innovative approach that addresses the problem of dataset constraints by integrating generative AI models. Traditional zero-shot learning methods are constrained by the training dataset. To fundamentally overcome this limitation, ODIN attempts to mitigate the dataset constraints by generating on-demand datas...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	351,004
2101.06590	Cost-Efficient Online Hyperparameter Optimization	Recent work on hyperparameters optimization (HPO) has shown the possibility of training certain hyperparameters together with regular parameters. However, these online HPO algorithms still require running evaluation on a set of validation examples at each training step, steeply increasing the training cost. To decide w...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	215,777
1711.06011	DIMAL: Deep Isometric Manifold Learning Using Sparse Geodesic Sampling	This paper explores a fully unsupervised deep learning approach for computing distance-preserving maps that generate low-dimensional embeddings for a certain class of manifolds. We use the Siamese configuration to train a neural network to solve the problem of least squares multidimensional scaling for generating maps ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	84,693
1304.0913	Predicting Network Attacks Using Ontology-Driven Inference	Graph knowledge models and ontologies are very powerful modeling and re asoning tools. We propose an effective approach to model network attacks and attack prediction which plays important roles in security management. The goals of this study are: First we model network attacks, their prerequisites and consequences usi...	false	false	false	false	true	false	false	false	false	false	false	false	true	false	false	false	false	true	23,423
2010.08213	Collaborative Training of GANs in Continuous and Discrete Spaces for Text Generation	Applying generative adversarial networks (GANs) to text-related tasks is challenging due to the discrete nature of language. One line of research resolves this issue by employing reinforcement learning (RL) and optimizing the next-word sampling policy directly in a discrete action space. Such methods compute the reward...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	201,109
2408.09431	Adversarial Attacked Teacher for Unsupervised Domain Adaptive Object Detection	Object detectors encounter challenges in handling domain shifts. Cutting-edge domain adaptive object detection methods use the teacher-student framework and domain adversarial learning to generate domain-invariant pseudo-labels for self-training. However, the pseudo-labels generated by the teacher model tend to be bias...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	481,435
2212.10375	Self-Adaptive In-Context Learning: An Information Compression Perspective for In-Context Example Selection and Ordering	Despite the surprising few-shot performance of in-context learning (ICL), it is still a common practice to randomly sample examples to serve as context. This paper advocates a new principle for ICL: self-adaptive in-context learning. The self-adaption mechanism is introduced to help each sample find an in-context examp...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	337,429
2106.07876	Vision-Language Navigation with Random Environmental Mixup	Vision-language Navigation (VLN) tasks require an agent to navigate step-by-step while perceiving the visual observations and comprehending a natural language instruction. Large data bias, which is caused by the disparity ratio between the small data scale and large navigation space, makes the VLN task challenging. Pre...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	241,090
2405.01065	MFDS-Net: Multi-Scale Feature Depth-Supervised Network for Remote Sensing Change Detection with Global Semantic and Detail Information	Change detection as an interdisciplinary discipline in the field of computer vision and remote sensing at present has been receiving extensive attention and research. Due to the rapid development of society, the geographic information captured by remote sensing satellites is changing faster and more complex, which undo...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	451,204
2411.04108	Weighted Sobolev Approximation Rates for Neural Networks on Unbounded Domains	In this work, we consider the approximation capabilities of shallow neural networks in weighted Sobolev spaces for functions in the spectral Barron space. The existing literature already covers several cases, in which the spectral Barron space can be approximated well, i.e., without curse of dimensionality, by shallow ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	506,150
2208.05539	Semi-supervised segmentation of tooth from 3D Scanned Dental Arches	Teeth segmentation is an important topic in dental restorations that is essential for crown generation, diagnosis, and treatment planning. In the dental field, the variability of input data is high and there are no publicly available 3D dental arch datasets. Although there has been improvement in the field provided by ...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	312,425
2410.17504	An Ontology-Enabled Approach For User-Centered and Knowledge-Enabled Explanations of AI Systems	Explainable Artificial Intelligence (AI) focuses on helping humans understand the working of AI systems or their decisions and has been a cornerstone of AI for decades. Recent research in explainability has focused on explaining the workings of AI models or model explainability. There have also been several position st...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	501,490

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