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
2409.15022	A Diagonal Structured State Space Model on Loihi 2 for Efficient Streaming Sequence Processing	Deep State-Space Models (SSM) demonstrate state-of-the art performance on long-range sequence modeling tasks. While the recurrent structure of SSMs can be efficiently implemented as a convolution or as a parallel scan during training, recurrent token-by-token processing cannot currently be implemented efficiently on GP...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	true	false	true	490,726
2304.06018	Adaptive Human Matting for Dynamic Videos	The most recent efforts in video matting have focused on eliminating trimap dependency since trimap annotations are expensive and trimap-based methods are less adaptable for real-time applications. Despite the latest tripmap-free methods showing promising results, their performance often degrades when dealing with high...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	357,819
2112.01036	GANSeg: Learning to Segment by Unsupervised Hierarchical Image Generation	Segmenting an image into its parts is a frequent preprocess for high-level vision tasks such as image editing. However, annotating masks for supervised training is expensive. Weakly-supervised and unsupervised methods exist, but they depend on the comparison of pairs of images, such as from multi-views, frames of video...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	269,346
1709.10282	Deep Competitive Pathway Networks	In the design of deep neural architectures, recent studies have demonstrated the benefits of grouping subnetworks into a larger network. For examples, the Inception architecture integrates multi-scale subnetworks and the residual network can be regarded that a residual unit combines a residual subnetwork with an identi...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	81,761
cs/0405047	Modular technology of developing of the problem-oriented extensions of a CAD system of reconstruction of the plant	The modular technology of creation of the problem-oriented extensions of a CAD system is described, which was realised in a system TechnoCAD GlassX for designing of reconstruction of the plants. The modularity of the technology is expressed in storage of all parameters of the design in one element of the drawing - modu...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	538,198
1911.01633	On the Importance of Location Privacy for Users of Location Based Applications	Do people care about their location privacy while using location-based service apps? This paper aims to answer this question and several other hypotheses through a survey, and review the privacy preservation techniques. Our results indicate that privacy is indeed an influential factor in the selection of location-based...	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	true	false	152,158
1806.03973	State Classification with CNN	There is a plenty of research going on in field of object recognition, but object state recognition has not been addressed as much. There are many important applications which can utilize object state recognition, such as, in robotics, to decide for how to grab an object. A convolution neural network was designed to cl...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	100,131
2206.01137	Finding the Right Recipe for Low Resource Domain Adaptation in Neural Machine Translation	General translation models often still struggle to generate accurate translations in specialized domains. To guide machine translation practitioners and characterize the effectiveness of domain adaptation methods under different data availability scenarios, we conduct an in-depth empirical exploration of monolingual an...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	300,365
2308.07837	CCD-3DR: Consistent Conditioning in Diffusion for Single-Image 3D Reconstruction	In this paper, we present a novel shape reconstruction method leveraging diffusion model to generate 3D sparse point cloud for the object captured in a single RGB image. Recent methods typically leverage global embedding or local projection-based features as the condition to guide the diffusion model. However, such str...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	385,664
2309.03986	Noisy Computing of the $\mathsf{OR}$ and $\mathsf{MAX}$ Functions	We consider the problem of computing a function of $n$ variables using noisy queries, where each query is incorrect with some fixed and known probability $p \in (0,1/2)$. Specifically, we consider the computation of the $\mathsf{OR}$ function of $n$ bits (where queries correspond to noisy readings of the bits) and the ...	false	false	false	false	true	false	true	false	false	true	false	false	false	false	false	false	false	true	390,576
2303.02972	Large-Scale Exploration of Cave Environments by Unmanned Aerial Vehicles	This paper presents a self-contained system for the robust utilization of aerial robots in the autonomous exploration of cave environments to help human explorers, first responders, and speleologists. The proposed system is generally applicable to an arbitrary exploration task within an unknown and unstructured subterr...	false	false	false	false	false	false	false	true	false	false	true	false	false	false	true	false	false	false	349,558
1110.1391	A Comparison of Different Machine Transliteration Models	Machine transliteration is a method for automatically converting words in one language into phonetically equivalent ones in another language. Machine transliteration plays an important role in natural language applications such as information retrieval and machine translation, especially for handling proper nouns and t...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	12,521
2202.07447	Trustworthy Autonomous Systems (TAS): Engaging TAS experts in curriculum design	Recent advances in artificial intelligence, specifically machine learning, contributed positively to enhancing the autonomous systems industry, along with introducing social, technical, legal and ethical challenges to make them trustworthy. Although Trustworthy Autonomous Systems (TAS) is an established and growing res...	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	false	false	280,552
1212.6456	A universal assortativity measure for network analysis	Characterizing the connectivity tendency of a network is a fundamental problem in network science. The traditional and well-known assortativity coefficient is calculated on a per-network basis, which is of little use to partial connection tendency of a network. This paper proposes a universal assortativity coefficient(...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	20,645
2205.07985	Expert Systems with Logic#. A Novel Modeling Framework for Logic Programming in an Object-Oriented Context of C#	We present a novel approach how logic programming for expert systems can be declared directly in an object-oriented language.	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	296,782
2206.12774	Meta Auxiliary Learning for Low-resource Spoken Language Understanding	Spoken language understanding (SLU) treats automatic speech recognition (ASR) and natural language understanding (NLU) as a unified task and usually suffers from data scarcity. We exploit an ASR and NLU joint training method based on meta auxiliary learning to improve the performance of low-resource SLU task by only ta...	false	false	true	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	304,729
2209.03427	Causal discovery for time series with latent confounders	Reconstructing the causal relationships behind the phenomena we observe is a fundamental challenge in all areas of science. Discovering causal relationships through experiments is often infeasible, unethical, or expensive in complex systems. However, increases in computational power allow us to process the ever-growing...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	316,489
2106.15352	Detecting Changed-Hands Online Review Accounts	A reputable social media or review account can be a good cover for spamming activities. It has become prevalent that spammers buy/sell such accounts openly on the Web. We call these sold/bought accounts the changed-hands (CH) accounts. They are hard to detect by existing spam detection algorithms as their spamming acti...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	243,733
2212.08649	Better May Not Be Fairer: A Study on Subgroup Discrepancy in Image Classification	In this paper, we provide 20,000 non-trivial human annotations on popular datasets as a first step to bridge gap to studying how natural semantic spurious features affect image classification, as prior works often study datasets mixing low-level features due to limitations in accessing realistic datasets. We investigat...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	336,825
2103.04250	Greedy Approximation Algorithms for Active Sequential Hypothesis Testing	In the problem of active sequential hypothesis testing (ASHT), a learner seeks to identify the true hypothesis from among a known set of hypotheses. The learner is given a set of actions and knows the random distribution of the outcome of any action under any true hypothesis. Given a target error $\delta>0$, the goal i...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	223,573
2108.02475	Cycle Analysis of Directed Acyclic Graphs	In this paper, we employ the decomposition of a directed network as an undirected graph plus its associated node metadata to characterise the cyclic structure found in directed networks by finding a Minimal Cycle Basis of the undirected graph and augment its components with direction information. We show that only four...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	249,336
2108.07920	Adversarial Relighting Against Face Recognition	Deep face recognition (FR) has achieved significantly high accuracy on several challenging datasets and fosters successful real-world applications, even showing high robustness to the illumination variation that is usually regarded as a main threat to the FR system. However, in the real world, illumination variation ca...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	251,061
2106.15298	US Fatal Police Shooting Analysis and Prediction	We believe that "all men are created equal". With the rise of the police shootings reported by media, more people in the U.S. think that police use excessive force during law enforcement, especially to a specific group of people. We want to apply multidimensional statistical analysis to reveal more facts than the monot...	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	false	243,693
2306.04932	Jigsaw-based Benchmarking for Learning Robotic Manipulation	Benchmarking provides experimental evidence of the scientific baseline to enhance the progression of fundamental research, which is also applicable to robotics. In this paper, we propose a method to benchmark metrics of robotic manipulation, which addresses the spatial-temporal reasoning skills for robot learning with ...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	371,972
2201.01811	CausalSim: A Causal Framework for Unbiased Trace-Driven Simulation	We present CausalSim, a causal framework for unbiased trace-driven simulation. Current trace-driven simulators assume that the interventions being simulated (e.g., a new algorithm) would not affect the validity of the traces. However, real-world traces are often biased by the choices algorithms make during trace collec...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	true	274,353
1111.5296	Analytical and Learning-Based Spectrum Sensing Time Optimization in Cognitive Radio Systems	Powerful spectrum sensing schemes enable cognitive radios (CRs) to find transmission opportunities in spectral resources allocated exclusively to the primary users. In this paper, maximizing the average throughput of a secondary user by optimizing its spectrum sensing time is formulated assuming that a prior knowledge ...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	13,133
2104.06147	What is the appropriate speed for an autonomous vehicle? Designing a Pedestrian Aware Contextual Speed Controller	Social acceptance is a major hurdle for autonomous vehicle technology, central to which is ensuring both passengers and nearby pedestrians feel safe. This idea of `feeling safe' and perceived safety is highly subjective and rooted in human intuition. As such, traditional analytical approaches to autonomous navigation o...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	229,975
2105.07066	Node Selection Toward Faster Convergence for Federated Learning on Non-IID Data	Federated Learning (FL) is a distributed learning paradigm that enables a large number of resource-limited nodes to collaboratively train a model without data sharing. The non-independent-and-identically-distributed (non-i.i.d.) data samples invoke discrepancies between the global and local objectives, making the FL mo...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	235,303
2010.15382	Learning to Actively Learn: A Robust Approach	This work proposes a procedure for designing algorithms for specific adaptive data collection tasks like active learning and pure-exploration multi-armed bandits. Unlike the design of traditional adaptive algorithms that rely on concentration of measure and careful analysis to justify the correctness and sample complex...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	203,753
2209.01676	Time-distance vision transformers in lung cancer diagnosis from longitudinal computed tomography	Features learned from single radiologic images are unable to provide information about whether and how much a lesion may be changing over time. Time-dependent features computed from repeated images can capture those changes and help identify malignant lesions by their temporal behavior. However, longitudinal medical im...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	315,974
1810.10850	An Adversarial Learning Approach to Medical Image Synthesis for Lesion Detection	The identification of lesion within medical image data is necessary for diagnosis, treatment and prognosis. Segmentation and classification approaches are mainly based on supervised learning with well-paired image-level or voxel-level labels. However, labeling the lesion in medical images is laborious requiring highly ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	111,377
2002.05878	An LSTM-Based Autonomous Driving Model Using Waymo Open Dataset	The Waymo Open Dataset has been released recently, providing a platform to crowdsource some fundamental challenges for automated vehicles (AVs), such as 3D detection and tracking. While~the dataset provides a large amount of high-quality and multi-source driving information, people in academia are more interested in th...	false	false	false	false	false	false	true	true	false	false	false	true	false	false	false	false	false	false	164,028
2012.07551	Towards unsupervised phone and word segmentation using self-supervised vector-quantized neural networks	We investigate segmenting and clustering speech into low-bitrate phone-like sequences without supervision. We specifically constrain pretrained self-supervised vector-quantized (VQ) neural networks so that blocks of contiguous feature vectors are assigned to the same code, thereby giving a variable-rate segmentation of...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	211,501
1509.05534	Tangential Interpolatory Projection for Model Reduction of Linear Quantum Stochastic Systems	This paper presents a model reduction method for the class of linear quantum stochastic systems often encountered in quantum optics and their related fields. The approach is proposed on the basis of an interpolatory projection ensuring that specific input-output responses of the original and the reduced-order systems a...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	47,062
2210.10203	From Model-Based to Model-Free: Learning Building Control for Demand Response	Grid-interactive building control is a challenging and important problem for reducing carbon emissions, increasing energy efficiency, and supporting the electric power grid. Currently researchers and practitioners are confronted with a choice of control strategies ranging from model-free (purely data-driven) to model-b...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	324,822
1005.0732	Outage rates and outage durations of opportunistic relaying systems	Opportunistic relaying is a simple yet efficient cooperation scheme that achieves full diversity and preserves the spectral efficiency among the spatially distributed stations. However, the stations' mobility causes temporal correlation of the system's capacity outage events, which gives rise to its important second-or...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	6,406
2111.10361	Solving Visual Analogies Using Neural Algorithmic Reasoning	We consider a class of visual analogical reasoning problems that involve discovering the sequence of transformations by which pairs of input/output images are related, so as to analogously transform future inputs. This program synthesis task can be easily solved via symbolic search. Using a variation of the `neural ana...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	267,295
2208.02439	MPPI-IPDDP: Hybrid Method of Collision-Free Smooth Trajectory Generation for Autonomous Robots	This paper presents a hybrid trajectory optimization method designed to generate collision-free, smooth trajectories for autonomous mobile robots. By combining sampling-based Model Predictive Path Integral (MPPI) control with gradient-based Interior-Point Differential Dynamic Programming (IPDDP), we leverage their resp...	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	311,466
2410.22844	Understanding and Improving Adversarial Collaborative Filtering for Robust Recommendation	Adversarial Collaborative Filtering (ACF), which typically applies adversarial perturbations at user and item embeddings through adversarial training, is widely recognized as an effective strategy for enhancing the robustness of Collaborative Filtering (CF) recommender systems against poisoning attacks. Besides, numero...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	503,793
2005.11429	Mechanisms for Outsourcing Computation via a Decentralized Market	As the number of personal computing and IoT devices grows rapidly, so does the amount of computational power that is available at the edge. Since many of these devices are often idle, there is a vast amount of computational power that is currently untapped, and which could be used for outsourcing computation. Existing ...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	true	178,469
2007.16088	Congestion Management for Mobility-on-Demand Schemes that use Electric Vehicles	To date the majority of commuters use their privately owned vehicle that uses an internal combustion engine. This transportation model suffers from low vehicle utilization and causes environmental pollution. This paper studies the use of Electric Vehicles (EVs) operating in a Mobility-on-Demand (MoD) scheme and tackles...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	189,841
1005.5466	Quantitative parametrization of texts written by Ivan Franko: An attempt of the project	In the article, the project of quantitative parametrization of all texts by Ivan Franko is manifested. It can be made only by using modern computer techniques after the frequency dictionaries for all Franko's works are compiled. The paper describes the application spheres, methodology, stages, principles and peculiarit...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	6,609
1504.03109	DVB-S2x Enabled Precoding for High Throughput Satellite Systems	Multi-user Multiple-Input Multiple-Output (MU-MIMO) has allowed recent releases of terrestrial LTE standards to achieve significant improvements in terms of offered system capacity. The publications of the DVB-S2x standard and particularly of its novel superframe structure is a key enabler for applying similar interfer...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	41,999
2301.07342	Parameter Estimation-Based Observer for Linear Systems with Polynomial Overparametrization	An adaptive state observer is proposed for a class of overparametrized uncertain linear time-invariant systems without restrictive requirement of their representation in the observer canonical form. It evolves the method of generalized parameters estimation-based observer design and, therefore, (i) does not require to ...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	340,897
2501.06414	IPP-Net: A Generalizable Deep Neural Network Model for Indoor Pathloss Radio Map Prediction	In this paper, we propose a generalizable deep neural network model for indoor pathloss radio map prediction (termed as IPP-Net). IPP-Net is based on a UNet architecture and learned from both large-scale ray tracing simulation data and a modified 3GPP indoor hotspot model. The performance of IPP-Net is evaluated in the...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	523,971
2106.09276	Uniform Convergence of Interpolators: Gaussian Width, Norm Bounds, and Benign Overfitting	We consider interpolation learning in high-dimensional linear regression with Gaussian data, and prove a generic uniform convergence guarantee on the generalization error of interpolators in an arbitrary hypothesis class in terms of the class's Gaussian width. Applying the generic bound to Euclidean norm balls recovers...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	241,608
2210.05912	PSNet: Parallel Symmetric Network for Video Salient Object Detection	For the video salient object detection (VSOD) task, how to excavate the information from the appearance modality and the motion modality has always been a topic of great concern. The two-stream structure, including an RGB appearance stream and an optical flow motion stream, has been widely used as a typical pipeline fo...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	323,054
1912.06075	Coronary Artery Plaque Characterization from CCTA Scans using Deep Learning and Radiomics	Assessing coronary artery plaque segments in coronary CT angiography scans is an important task to improve patient management and clinical outcomes, as it can help to decide whether invasive investigation and treatment are necessary. In this work, we present three machine learning approaches capable of performing this ...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	157,260
1903.06445	Applying Probabilistic Programming to Affective Computing	Affective Computing is a rapidly growing field spurred by advancements in artificial intelligence, but often, held back by the inability to translate psychological theories of emotion into tractable computational models. To address this, we propose a probabilistic programming approach to affective computing, which mode...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	124,387
2404.01753	M2SA: Multimodal and Multilingual Model for Sentiment Analysis of Tweets	In recent years, multimodal natural language processing, aimed at learning from diverse data types, has garnered significant attention. However, there needs to be more clarity when it comes to analysing multimodal tasks in multi-lingual contexts. While prior studies on sentiment analysis of tweets have predominantly fo...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	443,570
1612.08714	Clustering with Confidence: Finding Clusters with Statistical Guarantees	Clustering is a widely used unsupervised learning method for finding structure in the data. However, the resulting clusters are typically presented without any guarantees on their robustness; slightly changing the used data sample or re-running a clustering algorithm involving some stochastic component may lead to comp...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	66,107
2305.16657	Higher Order Gauge Equivariant CNNs on Riemannian Manifolds and Applications	With the advent of group equivariant convolutions in deep networks literature, spherical CNNs with $\mathsf{SO}(3)$-equivariant layers have been developed to cope with data that are samples of signals on the sphere $S^2$. One can implicitly obtain $\mathsf{SO}(3)$-equivariant convolutions on $S^2$ with significant effi...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	368,197
1312.4587	FFTPL: An Analytic Placement Algorithm Using Fast Fourier Transform for Density Equalization	We propose a flat nonlinear placement algorithm FFTPL using fast Fourier transform for density equalization. The placement instance is modeled as an electrostatic system with the analogy of density cost to the potential energy. A well-defined Poisson's equation is proposed for gradient and cost computation. Our placer ...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	29,152
2312.05391	Loss Functions in the Era of Semantic Segmentation: A Survey and Outlook	Semantic image segmentation, the process of classifying each pixel in an image into a particular class, plays an important role in many visual understanding systems. As the predominant criterion for evaluating the performance of statistical models, loss functions are crucial for shaping the development of deep learning...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	414,062
1304.7153	A Convex Approach for Image Hallucination	In this paper we propose a global convex approach for image hallucination. Altering the idea of classical multi image super resolution (SU) systems to single image SU, we incorporate aligned images to hallucinate the output. Our work is based on the paper of Tappen et al. where they use a non-convex model for image hal...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	24,225
2006.10672	Federated Learning With Quantized Global Model Updates	We study federated learning (FL), which enables mobile devices to utilize their local datasets to collaboratively train a global model with the help of a central server, while keeping data localized. At each iteration, the server broadcasts the current global model to the devices for local training, and aggregates the ...	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	true	182,966
2409.14128	Present and Future Generalization of Synthetic Image Detectors	The continued release of increasingly realistic image generation models creates a demand for synthetic image detectors. To build effective detectors we must first understand how factors like data source diversity, training methodologies and image alterations affect their generalization capabilities. This work conducts ...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	490,341
1710.02292	Asymptotic Performance of Coded Slotted ALOHA with Multi Packet Reception	In this letter, we develop a converse bound on the asymptotic load threshold of coded slotted ALOHA (CSA) schemes with K-multi packet reception capabilities at the receiver. Density evolution is used to track the average probability of packet segment loss and an area matching condition is applied to obtain the converse...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	82,151
2312.00029	Bergeron: Combating Adversarial Attacks through a Conscience-Based Alignment Framework	Research into AI alignment has grown considerably since the recent introduction of increasingly capable Large Language Models (LLMs). Unfortunately, modern methods of alignment still fail to fully prevent harmful responses when models are deliberately attacked. Such vulnerabilities can lead to LLMs being manipulated in...	false	false	false	false	true	false	false	false	true	false	false	false	true	false	false	false	false	false	411,858
0804.0041	On the reconstruction of block-sparse signals with an optimal number of measurements	Let A be an M by N matrix (M < N) which is an instance of a real random Gaussian ensemble. In compressed sensing we are interested in finding the sparsest solution to the system of equations A x = y for a given y. In general, whenever the sparsity of x is smaller than half the dimension of y then with overwhelming prob...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	1,511
2003.11001	Hybrid Classification and Reasoning for Image-based Constraint Solving	There is an increased interest in solving complex constrained problems where part of the input is not given as facts but received as raw sensor data such as images or speech. We will use "visual sudoku" as a prototype problem, where the given cell digits are handwritten and provided as an image thereof. In this case, o...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	169,488
1908.00734	Detection of Accounting Anomalies in the Latent Space using Adversarial Autoencoder Neural Networks	The detection of fraud in accounting data is a long-standing challenge in financial statement audits. Nowadays, the majority of applied techniques refer to handcrafted rules derived from known fraud scenarios. While fairly successful, these rules exhibit the drawback that they often fail to generalize beyond known frau...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	140,586
1606.09073	Locally Recoverable codes from rational maps	We give a method to construct Locally Recoverable Error-Correcting codes. This method is based on the use of rational maps between affine spaces. The recovery of erasures is carried out by Lagrangian interpolation in general and simply by one addition in some good cases.	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	57,943
2403.13144	Interactive Robot-Environment Self-Calibration via Compliant Exploratory Actions	Calibrating robots into their workspaces is crucial for manipulation tasks. Existing calibration techniques often rely on sensors external to the robot (cameras, laser scanners, etc.) or specialized tools. This reliance complicates the calibration process and increases the costs and time requirements. Furthermore, the ...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	439,491
2005.00680	Planar Symmetric Juggling of a Devil-Stick	Juggling a devil-stick can be described as a problem of non-prehensile manipulation. Assuming that the devil-stick remains confined to the vertical plane, the problem of juggling the stick between two symmetric configurations is considered. Impulsive forces are applied to the stick intermittently and the impulse of the...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	175,326
2207.10205	On the Robustness of 3D Object Detectors	In recent years, significant progress has been achieved for 3D object detection on point clouds thanks to the advances in 3D data collection and deep learning techniques. Nevertheless, 3D scenes exhibit a lot of variations and are prone to sensor inaccuracies as well as information loss during pre-processing. Thus, it ...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	309,169
2402.01681	Emojis Decoded: Leveraging ChatGPT for Enhanced Understanding in Social Media Communications	Emojis, which encapsulate semantics beyond mere words or phrases, have become prevalent in social network communications. This has spurred increasing scholarly interest in exploring their attributes and functionalities. However, emoji-related research and application face two primary challenges. First, researchers typi...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	426,135
2201.03017	Zero-Shot and Few-Shot Classification of Biomedical Articles in Context of the COVID-19 Pandemic	MeSH (Medical Subject Headings) is a large thesaurus created by the National Library of Medicine and used for fine-grained indexing of publications in the biomedical domain. In the context of the COVID-19 pandemic, MeSH descriptors have emerged in relation to articles published on the corresponding topic. Zero-shot cla...	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	274,727
2206.03659	Scalable Online Disease Diagnosis via Multi-Model-Fused Actor-Critic Reinforcement Learning	For those seeking healthcare advice online, AI based dialogue agents capable of interacting with patients to perform automatic disease diagnosis are a viable option. This application necessitates efficient inquiry of relevant disease symptoms in order to make accurate diagnosis recommendations. This can be formulated a...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	301,359
cs/0402030	Computational complexity and simulation of rare events of Ising spin glasses	We discuss the computational complexity of random 2D Ising spin glasses, which represent an interesting class of constraint satisfaction problems for black box optimization. Two extremal cases are considered: (1) the +/- J spin glass, and (2) the Gaussian spin glass. We also study a smooth transition between these two ...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	false	false	538,105
1710.07231	Modeling Graphs Using a Mixture of Kronecker Models	Generative models for graphs are increasingly becoming a popular tool for researchers to generate realistic approximations of graphs. While in the past, focus was on generating graphs which follow general laws, such as the power law for degree distribution, current models have the ability to learn from observed graphs ...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	82,901
1201.6530	Random Feature Maps for Dot Product Kernels	Approximating non-linear kernels using feature maps has gained a lot of interest in recent years due to applications in reducing training and testing times of SVM classifiers and other kernel based learning algorithms. We extend this line of work and present low distortion embeddings for dot product kernels into linear...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	14,017
1910.09200	Deep Reinforcement Learning Control of Quantum Cartpoles	We generalize a standard benchmark of reinforcement learning, the classical cartpole balancing problem, to the quantum regime by stabilizing a particle in an unstable potential through measurement and feedback. We use state-of-the-art deep reinforcement learning to stabilize a quantum cartpole and find that our deep le...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	150,111
2407.12164	Subject-driven Text-to-Image Generation via Preference-based Reinforcement Learning	Text-to-image generative models have recently attracted considerable interest, enabling the synthesis of high-quality images from textual prompts. However, these models often lack the capability to generate specific subjects from given reference images or to synthesize novel renditions under varying conditions. Methods...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	473,789
1707.03848	Reduced Electron Exposure for Energy-Dispersive Spectroscopy using Dynamic Sampling	Analytical electron microscopy and spectroscopy of biological specimens, polymers, and other beam sensitive materials has been a challenging area due to irradiation damage. There is a pressing need to develop novel imaging and spectroscopic imaging methods that will minimize such sample damage as well as reduce the dat...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	76,939
2110.09815	Microstructure reconstruction via artificial neural networks: A combination of causal and non-causal approach	We investigate the applicability of artificial neural networks (ANNs) in reconstructing a sample image of a sponge-like microstructure. We propose to reconstruct the image by predicting the phase of the current pixel based on its causal neighbourhood, and subsequently, use a non-causal ANN model to smooth out the recon...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	261,940
2306.07265	detrex: Benchmarking Detection Transformers	The DEtection TRansformer (DETR) algorithm has received considerable attention in the research community and is gradually emerging as a mainstream approach for object detection and other perception tasks. However, the current field lacks a unified and comprehensive benchmark specifically tailored for DETR-based models....	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	372,947
1602.07043	Auditing Black-box Models for Indirect Influence	Data-trained predictive models see widespread use, but for the most part they are used as black boxes which output a prediction or score. It is therefore hard to acquire a deeper understanding of model behavior, and in particular how different features influence the model prediction. This is important when interpreting...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	52,460
2309.01141	VGDiffZero: Text-to-image Diffusion Models Can Be Zero-shot Visual Grounders	Large-scale text-to-image diffusion models have shown impressive capabilities for generative tasks by leveraging strong vision-language alignment from pre-training. However, most vision-language discriminative tasks require extensive fine-tuning on carefully-labeled datasets to acquire such alignment, with great cost i...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	389,563
2312.16046	AdaNAS: Adaptively Post-processing with Self-supervised Neural Architecture Search for Ensemble Rainfall Forecasts	Previous post-processing studies on rainfall forecasts using numerical weather prediction (NWP) mainly focus on statistics-based aspects, while learning-based aspects are rarely investigated. Although some manually-designed models are proposed to raise accuracy, they are customized networks, which need to be repeatedly...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	418,246
1105.1302	A Modified Cross Correlation Algorithm for Reference-free Image Alignment of Non-Circular Projections in Single-Particle Electron Microscopy	In this paper we propose a modified cross correlation method to align images from the same class in single-particle electron microscopy of highly non-spherical structures. In this new method, First we coarsely align projection images, and then re-align the resulting images using the cross correlation (CC) method. The c...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	10,273
1804.00057	Understanding Autoencoders with Information Theoretic Concepts	Despite their great success in practical applications, there is still a lack of theoretical and systematic methods to analyze deep neural networks. In this paper, we illustrate an advanced information theoretic methodology to understand the dynamics of learning and the design of autoencoders, a special type of deep lea...	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	93,923
1802.03658	A geometric approach to integer factorization	We give a geometric approach to integer factorization. This approach is based on special approximations of segments of the curve that is represented by $y=n/x$, where $n$ is the integer whose factorization we need.	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	90,034
2402.00455	Tighter Lower Bounds on Aperiodic Ambiguity Function and Their Asymptotic Achievability	This paper presents tighter lower bounds on the maximum aperiodic ambiguity function (AF) magnitude of unimodular sequences under certain delay-Doppler low ambiguity zones (LAZ). These bounds are derived by exploiting the upper and lower bounds on the Frobenius norm of the weighted auto- and cross-AF matrices, with the...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	425,618
2309.11043	Score Mismatching for Generative Modeling	We propose a new score-based model with one-step sampling. Previously, score-based models were burdened with heavy computations due to iterative sampling. For substituting the iterative process, we train a standalone generator to compress all the time steps with the gradient backpropagated from the score network. In or...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	393,252
2106.01221	Differential Privacy for Text Analytics via Natural Text Sanitization	Texts convey sophisticated knowledge. However, texts also convey sensitive information. Despite the success of general-purpose language models and domain-specific mechanisms with differential privacy (DP), existing text sanitization mechanisms still provide low utility, as cursed by the high-dimensional text representa...	false	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	238,429
1712.00436	Unsupervised Learning for Color Constancy	Most digital camera pipelines use color constancy methods to reduce the influence of illumination and camera sensor on the colors of scene objects. The highest accuracy of color correction is obtained with learning-based color constancy methods, but they require a significant amount of calibrated training images with k...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	85,898
2106.12034	Pure Exploration in Kernel and Neural Bandits	We study pure exploration in bandits, where the dimension of the feature representation can be much larger than the number of arms. To overcome the curse of dimensionality, we propose to adaptively embed the feature representation of each arm into a lower-dimensional space and carefully deal with the induced model miss...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	242,591
2409.10831	PDMX: A Large-Scale Public Domain MusicXML Dataset for Symbolic Music Processing	The recent explosion of generative AI-Music systems has raised numerous concerns over data copyright, licensing music from musicians, and the conflict between open-source AI and large prestige companies. Such issues highlight the need for publicly available, copyright-free musical data, in which there is a large shorta...	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	false	true	488,896
2405.17902	Boosting Protein Language Models with Negative Sample Mining	We introduce a pioneering methodology for boosting large language models in the domain of protein representation learning. Our primary contribution lies in the refinement process for correlating the over-reliance on co-evolution knowledge, in a way that networks are trained to distill invaluable insights from negative ...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	458,179
2006.13546	Crossmodal Language Grounding in an Embodied Neurocognitive Model	Human infants are able to acquire natural language seemingly easily at an early age. Their language learning seems to occur simultaneously with learning other cognitive functions as well as with playful interactions with the environment and caregivers. From a neuroscientific perspective, natural language is embodied, g...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	true	false	false	183,950
1701.01007	Single Letter Expression of Capacity for a Class of Channels with Memory	We study finite alphabet channels with Unit Memory on the previous Channel Outputs called UMCO channels. We identify necessary and sufficient conditions, to test whether the capacity achieving channel input distributions with feedback are time-invariant, and whether feedback capacity is characterized by single letter, ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	66,345
2008.01999	F2GAN: Fusing-and-Filling GAN for Few-shot Image Generation	In order to generate images for a given category, existing deep generative models generally rely on abundant training images. However, extensive data acquisition is expensive and fast learning ability from limited data is necessarily required in real-world applications. Also, these existing methods are not well-suited ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	190,496
2402.15200	DeMPT: Decoding-enhanced Multi-phase Prompt Tuning for Making LLMs Be Better Context-aware Translators	Generally, the decoder-only large language models (LLMs) are adapted to context-aware neural machine translation (NMT) in a concatenating way, where LLMs take the concatenation of the source sentence (i.e., intra-sentence context) and the inter-sentence context as the input, and then to generate the target tokens seque...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	432,038
1701.00251	Outlier Robust Online Learning	We consider the problem of learning from noisy data in practical settings where the size of data is too large to store on a single machine. More challenging, the data coming from the wild may contain malicious outliers. To address the scalability and robustness issues, we present an online robust learning (ORL) approac...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	66,250
2101.10213	A Trigger-Sense Memory Flow Framework for Joint Entity and Relation Extraction	Joint entity and relation extraction framework constructs a unified model to perform entity recognition and relation extraction simultaneously, which can exploit the dependency between the two tasks to mitigate the error propagation problem suffered by the pipeline model. Current efforts on joint entity and relation ex...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	216,855
1707.08423	Non-Stationary Bandits with Habituation and Recovery Dynamics	Many settings involve sequential decision-making where a set of actions can be chosen at each time step, each action provides a stochastic reward, and the distribution for the reward of each action is initially unknown. However, frequent selection of a specific action may reduce its expected reward, while abstaining fr...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	77,831
1902.06958	On the Analysis of EM for truncated mixtures of two Gaussians	Motivated by a recent result of Daskalakis et al. 2018, we analyze the population version of Expectation-Maximization (EM) algorithm for the case of \textit{truncated} mixtures of two Gaussians. Truncated samples from a $d$-dimensional mixture of two Gaussians $\frac{1}{2} \mathcal{N}(\vec{\mu}, \vec{\Sigma})+ \frac{1}...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	121,886
2501.03402	On the Adversarial Robustness of Benjamini Hochberg	The Benjamini-Hochberg (BH) procedure is widely used to control the false detection rate (FDR) in multiple testing. Applications of this control abound in drug discovery, forensics, anomaly detection, and, in particular, machine learning, ranging from nonparametric outlier detection to out-of-distribution detection and...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	522,858
1809.06684	Average performance of Orthogonal Matching Pursuit (OMP) for sparse approximation	We present a theoretical analysis of the average performance of OMP for sparse approximation. For signals that are generated from a dictionary with $K$ atoms and coherence $\mu$ and coefficients corresponding to a geometric sequence with parameter $\alpha<1$, we show that OMP is successful with high probability as long...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	108,115

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