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Title: Shape complexity in cluster analysis Abstract: In cluster analysis, a common first step is to scale the data aiming to better partition them into clusters. Even though many different techniques have throughout many years been introduced to this end, it is probably fair to say that the workhorse in this preproces... |
Title: Perfect Spectral Clustering with Discrete Covariates Abstract: Among community detection methods, spectral clustering enjoys two desirable properties: computational efficiency and theoretical guarantees of consistency. Most studies of spectral clustering consider only the edges of a network as input to the algor... |
Title: HelixADMET: a robust and endpoint extensible ADMET system incorporating self-supervised knowledge transfer Abstract: Accurate ADMET (an abbreviation for "absorption, distribution, metabolism, excretion, and toxicity") predictions can efficiently screen out undesirable drug candidates in the early stage of drug d... |
Title: "What makes a question inquisitive?" A Study on Type-Controlled Inquisitive Question Generation Abstract: We propose a type-controlled framework for inquisitive question generation. We annotate an inquisitive question dataset with question types, train question type classifiers, and finetune models for type-cont... |
Title: Robust Perception Architecture Design for Automotive Cyber-Physical Systems Abstract: In emerging automotive cyber-physical systems (CPS), accurate environmental perception is critical to achieving safety and performance goals. Enabling robust perception for vehicles requires solving multiple complex problems re... |
Title: A Framework for CSI-Based Indoor Localization with 1D Convolutional Neural Networks Abstract: Modern indoor localization techniques are essential to overcome the weak GPS coverage in indoor environments. Recently, considerable progress has been made in Channel State Information (CSI) based indoor localization wi... |
Title: Multi-Head Attention Neural Network for Smartphone Invariant Indoor Localization Abstract: Smartphones together with RSSI fingerprinting serve as an efficient approach for delivering a low-cost and high-accuracy indoor localization solution. However, a few critical challenges have prevented the wide-spread proli... |
Title: A Survey on Machine Learning for Geo-Distributed Cloud Data Center Management Abstract: Cloud workloads today are typically managed in a distributed environment and processed across geographically distributed data centers. Cloud service providers have been distributing data centers globally to reduce operating c... |
Title: Unraveling Attention via Convex Duality: Analysis and Interpretations of Vision Transformers Abstract: Vision transformers using self-attention or its proposed alternatives have demonstrated promising results in many image related tasks. However, the underpinning inductive bias of attention is not well understoo... |
Title: Can Bad Teaching Induce Forgetting? Unlearning in Deep Networks using an Incompetent Teacher Abstract: Machine unlearning has become an important field of research due to an increasing focus on addressing the evolving data privacy rules and regulations into the machine learning (ML) applications. It facilitates ... |
Title: Can We Do Better Than Random Start? The Power of Data Outsourcing Abstract: Many organizations have access to abundant data but lack the computational power to process the data. While they can outsource the computational task to other facilities, there are various constraints on the amount of data that can be sh... |
Title: Dimensionality Reduced Training by Pruning and Freezing Parts of a Deep Neural Network, a Survey Abstract: State-of-the-art deep learning models have a parameter count that reaches into the billions. Training, storing and transferring such models is energy and time consuming, thus costly. A big part of these cos... |
Title: Computerized Tomography Pulmonary Angiography Image Simulation using Cycle Generative Adversarial Network from Chest CT imaging in Pulmonary Embolism Patients Abstract: The purpose of this research is to develop a system that generates simulated computed tomography pulmonary angiography (CTPA) images clinically ... |
Title: Forecasting Solar Power Generation on the basis of Predictive and Corrective Maintenance Activities Abstract: Solar energy forecasting has seen tremendous growth in the last decade using historical time series collected from a weather station, such as weather variables wind speed and direction, solar radiance, a... |
Title: Fast and Provably Convergent Algorithms for Gromov-Wasserstein in Graph Learning Abstract: In this paper, we study the design and analysis of a class of efficient algorithms for computing the Gromov-Wasserstein (GW) distance tailored to large-scale graph learning tasks. Armed with the Luo-Tseng error bound condi... |
Title: ShiftAddNAS: Hardware-Inspired Search for More Accurate and Efficient Neural Networks Abstract: Neural networks (NNs) with intensive multiplications (e.g., convolutions and transformers) are capable yet power hungry, impeding their more extensive deployment into resource-constrained devices. As such, multiplicat... |
Title: Planning to Practice: Efficient Online Fine-Tuning by Composing Goals in Latent Space Abstract: General-purpose robots require diverse repertoires of behaviors to complete challenging tasks in real-world unstructured environments. To address this issue, goal-conditioned reinforcement learning aims to acquire pol... |
Title: Latent Variable Method Demonstrator -- Software for Understanding Multivariate Data Analytics Algorithms Abstract: The ever-increasing quantity of multivariate process data is driving a need for skilled engineers to analyze, interpret, and build models from such data. Multivariate data analytics relies heavily o... |
Title: Brachial Plexus Nerve Trunk Segmentation Using Deep Learning: A Comparative Study with Doctors' Manual Segmentation Abstract: Ultrasound-guided nerve block anesthesia (UGNB) is a high-tech visual nerve block anesthesia method that can observe the target nerve and its surrounding structures, the puncture needle's... |
Title: Uncertainty-based Network for Few-shot Image Classification Abstract: The transductive inference is an effective technique in the few-shot learning task, where query sets update prototypes to improve themselves. However, these methods optimize the model by considering only the classification scores of the query ... |
Title: CellTypeGraph: A New Geometric Computer Vision Benchmark Abstract: Classifying all cells in an organ is a relevant and difficult problem from plant developmental biology. We here abstract the problem into a new benchmark for node classification in a geo-referenced graph. Solving it requires learning the spatial ... |
Title: On the Convergence of Policy in Unregularized Policy Mirror Descent Abstract: In this short note, we give the convergence analysis of the policy in the recent famous policy mirror descent (PMD). We mainly consider the unregularized setting following [11] with generalized Bregman divergence. The difference is tha... |
Title: Active learning of causal probability trees Abstract: The past two decades have seen a growing interest in combining causal information, commonly represented using causal graphs, with machine learning models. Probability trees provide a simple yet powerful alternative representation of causal information. They e... |
Title: SAMU-XLSR: Semantically-Aligned Multimodal Utterance-level Cross-Lingual Speech Representation Abstract: We propose the SAMU-XLSR: Semantically-Aligned Multimodal Utterance-level Cross-Lingual Speech Representation learning framework. Unlike previous works on speech representation learning, which learns multilin... |
Title: SKILL: Structured Knowledge Infusion for Large Language Models Abstract: Large language models (LLMs) have demonstrated human-level performance on a vast spectrum of natural language tasks. However, it is largely unexplored whether they can better internalize knowledge from a structured data, such as a knowledge... |
Title: Deep neural networks with dependent weights: Gaussian Process mixture limit, heavy tails, sparsity and compressibility Abstract: This article studies the infinite-width limit of deep feedforward neural networks whose weights are dependent, and modelled via a mixture of Gaussian distributions. Each hidden node of... |
Title: Automatic Acquisition of a Repertoire of Diverse Grasping Trajectories through Behavior Shaping and Novelty Search Abstract: Grasping a particular object may require a dedicated grasping movement that may also be specific to the robot end-effector. No generic and autonomous method does exist to generate these mo... |
Title: Moral reinforcement learning using actual causation Abstract: Reinforcement learning systems will to a greater and greater extent make decisions that significantly impact the well-being of humans, and it is therefore essential that these systems make decisions that conform to our expectations of morally good beh... |
Title: Sharp asymptotics on the compression of two-layer neural networks Abstract: In this paper, we study the compression of a target two-layer neural network with N nodes into a compressed network with M < N nodes. More precisely, we consider the setting in which the weights of the target network are i.i.d. sub-Gauss... |
Title: An Application of Scenario Exploration to Find New Scenarios for the Development and Testing of Automated Driving Systems in Urban Scenarios Abstract: Verification and validation are major challenges for developing automated driving systems. A concept that gets more and more recognized for testing in automated d... |
Title: blob loss: instance imbalance aware loss functions for semantic segmentation Abstract: Deep convolutional neural networks have proven to be remarkably effective in semantic segmentation tasks. Most popular loss functions were introduced targeting improved volumetric scores, such as the Sorensen Dice coefficient.... |
Title: ROP inception: signal estimation with quadratic random sketching Abstract: Rank-one projections (ROP) of matrices and quadratic random sketching of signals support several data processing and machine learning methods, as well as recent imaging applications, such as phase retrieval or optical processing units. In... |
Title: Hyper-Learning for Gradient-Based Batch Size Adaptation Abstract: Scheduling the batch size to increase is an effective strategy to control gradient noise when training deep neural networks. Current approaches implement scheduling heuristics that neglect structure within the optimization procedure, limiting thei... |
Title: Delaytron: Efficient Learning of Multiclass Classifiers with Delayed Bandit Feedbacks Abstract: In this paper, we present online algorithm called {\it Delaytron} for learning multi class classifiers using delayed bandit feedbacks. The sequence of feedback delays $\{d_t\}_{t=1}^T$ is unknown to the algorithm. At ... |
Title: IIsy: Practical In-Network Classification Abstract: The rat race between user-generated data and data-processing systems is currently won by data. The increased use of machine learning leads to further increase in processing requirements, while data volume keeps growing. To win the race, machine learning needs t... |
Title: Monotonicity Regularization: Improved Penalties and Novel Applications to Disentangled Representation Learning and Robust Classification Abstract: We study settings where gradient penalties are used alongside risk minimization with the goal of obtaining predictors satisfying different notions of monotonicity. Sp... |
Title: Adaptive Momentum-Based Policy Gradient with Second-Order Information Abstract: The variance reduced gradient estimators for policy gradient methods has been one of the main focus of research in the reinforcement learning in recent years as they allow acceleration of the estimation process. We propose a variance... |
Title: Multiscale reconstruction of porous media based on multiple dictionaries learning Abstract: Digital modeling of the microstructure is important for studying the physical and transport properties of porous media. Multiscale modeling for porous media can accurately characterize macro-pores and micro-pores in a lar... |
Title: KGNN: Distributed Framework for Graph Neural Knowledge Representation Abstract: Knowledge representation learning has been commonly adopted to incorporate knowledge graph (KG) into various online services. Although existing knowledge representation learning methods have achieved considerable performance improvem... |
Title: Measuring Alignment Bias in Neural Seq2Seq Semantic Parsers Abstract: Prior to deep learning the semantic parsing community has been interested in understanding and modeling the range of possible word alignments between natural language sentences and their corresponding meaning representations. Sequence-to-seque... |
Title: Semi-Parametric Contextual Bandits with Graph-Laplacian Regularization Abstract: Non-stationarity is ubiquitous in human behavior and addressing it in the contextual bandits is challenging. Several works have addressed the problem by investigating semi-parametric contextual bandits and warned that ignoring non-s... |
Title: Bayesian Physics-Informed Neural Networks for real-world nonlinear dynamical systems Abstract: Understanding real-world dynamical phenomena remains a challenging task. Across various scientific disciplines, machine learning has advanced as the go-to technology to analyze nonlinear dynamical systems, identify pat... |
Title: A Study of the Attention Abnormality in Trojaned BERTs Abstract: Trojan attacks raise serious security concerns. In this paper, we investigate the underlying mechanism of Trojaned BERT models. We observe the attention focus drifting behavior of Trojaned models, i.e., when encountering an poisoned input, the trig... |
Title: Accurate Machine Learned Quantum-Mechanical Force Fields for Biomolecular Simulations Abstract: Molecular dynamics (MD) simulations allow atomistic insights into chemical and biological processes. Accurate MD simulations require computationally demanding quantum-mechanical calculations, being practically limited... |
Title: Finite Element Method-enhanced Neural Network for Forward and Inverse Problems Abstract: We introduce a novel hybrid methodology combining classical finite element methods (FEM) with neural networks to create a well-performing and generalizable surrogate model for forward and inverse problems. The residual from ... |
Title: Scalable algorithms for physics-informed neural and graph networks Abstract: Physics-informed machine learning (PIML) has emerged as a promising new approach for simulating complex physical and biological systems that are governed by complex multiscale processes for which some data are also available. In some in... |
Title: Explanation-Guided Fairness Testing through Genetic Algorithm Abstract: The fairness characteristic is a critical attribute of trusted AI systems. A plethora of research has proposed diverse methods for individual fairness testing. However, they are suffering from three major limitations, i.e., low efficiency, l... |
Title: A unified framework for dataset shift diagnostics Abstract: Most machine learning (ML) methods assume that the data used in the training phase comes from the distribution of the target population. However, in practice one often faces dataset shift, which, if not properly taken into account, may decrease the pred... |
Title: Topological Signal Processing using the Weighted Ordinal Partition Network Abstract: One of the most important problems arising in time series analysis is that of bifurcation, or change point detection. That is, given a collection of time series over a varying parameter, when has the structure of the underlying ... |
Title: Demystifying the Data Need of ML-surrogates for CFD Simulations Abstract: Computational fluid dynamics (CFD) simulations, a critical tool in various engineering applications, often require significant time and compute power to predict flow properties. The high computational cost associated with CFD simulations s... |
Title: DouFu: A Double Fusion Joint Learning Method For Driving Trajectory Representation Abstract: Driving trajectory representation learning is of great significance for various location-based services, such as driving pattern mining and route recommendation. However, previous representation generation approaches ten... |
Title: Perturbation of Deep Autoencoder Weights for Model Compression and Classification of Tabular Data Abstract: Fully connected deep neural networks (DNN) often include redundant weights leading to overfitting and high memory requirements. Additionally, the performance of DNN is often challenged by traditional machi... |
Title: LPC-AD: Fast and Accurate Multivariate Time Series Anomaly Detection via Latent Predictive Coding Abstract: This paper proposes LPC-AD, a fast and accurate multivariate time series (MTS) anomaly detection method. LPC-AD is motivated by the ever-increasing needs for fast and accurate MTS anomaly detection methods... |
Title: REAL ML: Recognizing, Exploring, and Articulating Limitations of Machine Learning Research Abstract: Transparency around limitations can improve the scientific rigor of research, help ensure appropriate interpretation of research findings, and make research claims more credible. Despite these benefits, the machi... |
Title: Network Gradient Descent Algorithm for Decentralized Federated Learning Abstract: We study a fully decentralized federated learning algorithm, which is a novel gradient descent algorithm executed on a communication-based network. For convenience, we refer to it as a network gradient descent (NGD) method. In the ... |
Title: Deep Supervised Information Bottleneck Hashing for Cross-modal Retrieval based Computer-aided Diagnosis Abstract: Mapping X-ray images, radiology reports, and other medical data as binary codes in the common space, which can assist clinicians to retrieve pathology-related data from heterogeneous modalities (i.e.... |
Title: Applications of Reinforcement Learning in Deregulated Power Market: A Comprehensive Review Abstract: The increasing penetration of renewable generations, along with the deregulation and marketization of power industry, promotes the transformation of power market operation paradigms. The optimal bidding strategy ... |
Title: Individualized Risk Assessment of Preoperative Opioid Use by Interpretable Neural Network Regression Abstract: Preoperative opioid use has been reported to be associated with higher preoperative opioid demand, worse postoperative outcomes, and increased postoperative healthcare utilization and expenditures. Unde... |
Title: Evaluation of a User Authentication Schema Using Behavioral Biometrics and Machine Learning Abstract: The amount of secure data being stored on mobile devices has grown immensely in recent years. However, the security measures protecting this data have stayed static, with few improvements being done to the vulne... |
Title: Automatic Velocity Picking Using Unsupervised Ensemble Learning Abstract: In seismic data processing, accurate and efficient automatic velocity picking algorithms can significantly accelerate the processing, and the main branch is to use velocity spectra for velocity pickup. Recently, machine learning algorithms... |
Title: Should attention be all we need? The epistemic and ethical implications of unification in machine learning Abstract: "Attention is all you need" has become a fundamental precept in machine learning research. Originally designed for machine translation, transformers and the attention mechanisms that underpin them... |
Title: Machine learning and atomic layer deposition: predicting saturation times from reactor growth profiles using artificial neural networks Abstract: In this work we explore the application of deep neural networks to the optimization of atomic layer deposition processes based on thickness values obtained at differen... |
Title: Compatible deep neural network framework with financial time series data, including data preprocessor, neural network model and trading strategy Abstract: Experience has shown that trading in stock and cryptocurrency markets has the potential to be highly profitable. In this light, considerable effort has been r... |
Title: Bias and Fairness on Multimodal Emotion Detection Algorithms Abstract: Numerous studies have shown that machine learning algorithms can latch onto protected attributes such as race and gender and generate predictions that systematically discriminate against one or more groups. To date the majority of bias and fa... |
Title: Deep Learning of Chaotic Systems from Partially-Observed Data Abstract: Recently, a general data driven numerical framework has been developed for learning and modeling of unknown dynamical systems using fully- or partially-observed data. The method utilizes deep neural networks (DNNs) to construct a model for t... |
Title: Feedback Gradient Descent: Efficient and Stable Optimization with Orthogonality for DNNs Abstract: The optimization with orthogonality has been shown useful in training deep neural networks (DNNs). To impose orthogonality on DNNs, both computational efficiency and stability are important. However, existing metho... |
Title: How do Variational Autoencoders Learn? Insights from Representational Similarity Abstract: The ability of Variational Autoencoders (VAEs) to learn disentangled representations has made them popular for practical applications. However, their behaviour is not yet fully understood. For example, the questions of whe... |
Title: Automated Mobility Context Detection with Inertial Signals Abstract: Remote monitoring of motor functions is a powerful approach for health assessment, especially among the elderly population or among subjects affected by pathologies that negatively impact their walking capabilities. This is further supported by... |
Title: Fault Detection for Non-Condensing Boilers using Simulated Building Automation System Sensor Data Abstract: Building performance has been shown to degrade significantly after commissioning, resulting in increased energy consumption and associated greenhouse gas emissions. Continuous Commissioning using existing ... |
Title: Human Emotion Classification based on EEG Signals Using Recurrent Neural Network And KNN Abstract: In human contact, emotion is very crucial. Attributes like words, voice intonation, facial expressions, and kinesics can all be used to portray one's feelings. However, brain-computer interface (BCI) devices have n... |
Title: JUNO: Jump-Start Reinforcement Learning-based Node Selection for UWB Indoor Localization Abstract: Ultra-Wideband (UWB) is one of the key technologies empowering the Internet of Thing (IoT) concept to perform reliable, energy-efficient, and highly accurate monitoring, screening, and localization in indoor enviro... |
Title: Can You Still See Me?: Reconstructing Robot Operations Over End-to-End Encrypted Channels Abstract: Connected robots play a key role in Industry 4.0, providing automation and higher efficiency for many industrial workflows. Unfortunately, these robots can leak sensitive information regarding these operational wo... |
Title: DNNR: Differential Nearest Neighbors Regression Abstract: K-nearest neighbors (KNN) is one of the earliest and most established algorithms in machine learning. For regression tasks, KNN averages the targets within a neighborhood which poses a number of challenges: the neighborhood definition is crucial for the p... |
Title: Conditional Visual Servoing for Multi-Step Tasks Abstract: Visual Servoing has been effectively used to move a robot into specific target locations or to track a recorded demonstration. It does not require manual programming, but it is typically limited to settings where one demonstration maps to one environment... |
Title: On the Privacy of Decentralized Machine Learning Abstract: In this work, we carry out the first, in-depth, privacy analysis of Decentralized Learning -- a collaborative machine learning framework aimed at circumventing the main limitations of federated learning. We identify the decentralized learning properties ... |
Title: A Psychological Theory of Explainability Abstract: The goal of explainable Artificial Intelligence (XAI) is to generate human-interpretable explanations, but there are no computationally precise theories of how humans interpret AI generated explanations. The lack of theory means that validation of XAI must be do... |
Title: Utterance Weighted Multi-Dilation Temporal Convolutional Networks for Monaural Speech Dereverberation Abstract: Speech dereverberation is an important stage in many speech technology applications. Recent work in this area has been dominated by deep neural network models. Temporal convolutional networks (TCNs) ar... |
Title: Dynamic Recognition of Speakers for Consent Management by Contrastive Embedding Replay Abstract: Voice assistants record sound and can overhear conversations. Thus, a consent management mechanism is desirable such that users can express their wish to be recorded or not. Consent management can be implemented usin... |
Title: Robust Losses for Learning Value Functions Abstract: Most value function learning algorithms in reinforcement learning are based on the mean squared (projected) Bellman error. However, squared errors are known to be sensitive to outliers, both skewing the solution of the objective and resulting in high-magnitude... |
Title: Application of Graph Based Features in Computer Aided Diagnosis for Histopathological Image Classification of Gastric Cancer Abstract: The gold standard for gastric cancer detection is gastric histopathological image analysis, but there are certain drawbacks in the existing histopathological detection and diagno... |
Title: An Evaluation Framework for Legal Document Summarization Abstract: A law practitioner has to go through numerous lengthy legal case proceedings for their practices of various categories, such as land dispute, corruption, etc. Hence, it is important to summarize these documents, and ensure that summaries contain ... |
Title: Experimentally realized in situ backpropagation for deep learning in nanophotonic neural networks Abstract: Neural networks are widely deployed models across many scientific disciplines and commercial endeavors ranging from edge computing and sensing to large-scale signal processing in data centers. The most eff... |
Title: Recovering Private Text in Federated Learning of Language Models Abstract: Federated learning allows distributed users to collaboratively train a model while keeping each user's data private. Recently, a growing body of work has demonstrated that an eavesdropping attacker can effectively recover image data from ... |
Title: Do Neural Networks Compress Manifolds Optimally? Abstract: Artificial Neural-Network-based (ANN-based) lossy compressors have recently obtained striking results on several sources. Their success may be ascribed to an ability to identify the structure of low-dimensional manifolds in high-dimensional ambient space... |
Title: Supervised Learning for Coverage-Directed Test Selection in Simulation-Based Verification Abstract: Constrained random test generation is one the most widely adopted methods for generating stimuli for simulation-based verification. Randomness leads to test diversity, but tests tend to repeatedly exercise the sam... |
Title: High-dimensional additive Gaussian processes under monotonicity constraints Abstract: We introduce an additive Gaussian process framework accounting for monotonicity constraints and scalable to high dimensions. Our contributions are threefold. First, we show that our framework enables to satisfy the constraints ... |
Title: High-resolution landscape-scale biomass mapping using a spatiotemporal patchwork of LiDAR coverages Abstract: Estimating forest aboveground biomass at fine spatial scales has become increasingly important for greenhouse gas estimation, monitoring, and verification efforts to mitigate climate change. Airborne LiD... |
Title: Disentangling Visual Embeddings for Attributes and Objects Abstract: We study the problem of compositional zero-shot learning for object-attribute recognition. Prior works use visual features extracted with a backbone network, pre-trained for object classification and thus do not capture the subtly distinct feat... |
Title: Learning Quantum Entanglement Distillation with Noisy Classical Communications Abstract: Quantum networking relies on the management and exploitation of entanglement. Practical sources of entangled qubits are imperfect, producing mixed quantum state with reduced fidelity with respect to ideal Bell pairs. Therefo... |
Title: Strategizing against Learners in Bayesian Games Abstract: We study repeated two-player games where one of the players, the learner, employs a no-regret learning strategy, while the other, the optimizer, is a rational utility maximizer. We consider general Bayesian games, where the payoffs of both the optimizer a... |
Title: Label-Efficient Self-Supervised Federated Learning for Tackling Data Heterogeneity in Medical Imaging Abstract: The curation of large-scale medical datasets from multiple institutions necessary for training deep learning models is challenged by the difficulty in sharing patient data with privacy-preserving. Fede... |
Title: The Power of Reuse: A Multi-Scale Transformer Model for Structural Dynamic Segmentation in Symbolic Music Generation Abstract: Symbolic Music Generation relies on the contextual representation capabilities of the generative model, where the most prevalent approach is the Transformer-based model. Not only that, t... |
Title: CV4Code: Sourcecode Understanding via Visual Code Representations Abstract: We present CV4Code, a compact and effective computer vision method for sourcecode understanding. Our method leverages the contextual and the structural information available from the code snippet by treating each snippet as a two-dimensi... |
Title: Hierarchical Distribution-Aware Testing of Deep Learning Abstract: With its growing use in safety/security-critical applications, Deep Learning (DL) has raised increasing concerns regarding its dependability. In particular, DL has a notorious problem of lacking robustness. Despite recent efforts made in detectin... |
Title: Quantum Transfer Learning for Wi-Fi Sensing Abstract: Beyond data communications, commercial-off-the-shelf Wi-Fi devices can be used to monitor human activities, track device locomotion, and sense the ambient environment. In particular, spatial beam attributes that are inherently available in the 60-GHz IEEE 802... |
Title: Deep Neural Network Classifier for Multi-dimensional Functional Data Abstract: We propose a new approach, called as functional deep neural network (FDNN), for classifying multi-dimensional functional data. Specifically, a deep neural network is trained based on the principle components of the training data which... |
Title: Universal characteristics of deep neural network loss surfaces from random matrix theory Abstract: This paper considers several aspects of random matrix universality in deep neural networks. Motivated by recent experimental work, we use universal properties of random matrices related to local statistics to deriv... |
Title: Variational Quantum Compressed Sensing for Joint User and Channel State Acquisition in Grant-Free Device Access Systems Abstract: This paper introduces a new quantum computing framework integrated with a two-step compressed sensing technique, applied to a joint channel estimation and user identification problem.... |
Title: OneAligner: Zero-shot Cross-lingual Transfer with One Rich-Resource Language Pair for Low-Resource Sentence Retrieval Abstract: Aligning parallel sentences in multilingual corpora is essential to curating data for downstream applications such as Machine Translation. In this work, we present OneAligner, an alignm... |
Title: Multibit Tries Packet Classification with Deep Reinforcement Learning Abstract: High performance packet classification is a key component to support scalable network applications like firewalls, intrusion detection, and differentiated services. With ever increasing in the line-rate in core networks, it becomes a... |
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