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Title: Multiplier with Reduced Activities and Minimized Interconnect for Inner Product Arrays Abstract: We present a pipelined multiplier with reduced activities and minimized interconnect based on online digit-serial arithmetic. The working precision has been truncated such that $p<n$ bits are used to compute $n$ bits... |
Title: Entropy-based Stability-Plasticity for Lifelong Learning Abstract: The ability to continuously learn remains elusive for deep learning models. Unlike humans, models cannot accumulate knowledge in their weights when learning new tasks, mainly due to an excess of plasticity and the low incentive to reuse weights w... |
Title: A Data-Driven Method for Automated Data Superposition with Applications in Soft Matter Science Abstract: The superposition of data sets with internal parametric self-similarity is a longstanding and widespread technique for the analysis of many types of experimental data across the physical sciences. Typically, ... |
Title: Gaussian mixture modeling of nodes in Bayesian network according to maximal parental cliques Abstract: This paper uses Gaussian mixture model instead of linear Gaussian model to fit the distribution of every node in Bayesian network. We will explain why and how we use Gaussian mixture models in Bayesian network.... |
Title: De-biasing facial detection system using VAE Abstract: Bias in AI/ML-based systems is a ubiquitous problem and bias in AI/ML systems may negatively impact society. There are many reasons behind a system being biased. The bias can be due to the algorithm we are using for our problem or may be due to the dataset w... |
Title: Understanding and Preventing Capacity Loss in Reinforcement Learning Abstract: The reinforcement learning (RL) problem is rife with sources of non-stationarity, making it a notoriously difficult problem domain for the application of neural networks. We identify a mechanism by which non-stationary prediction targ... |
Title: Cross-view Brain Decoding Abstract: How the brain captures the meaning of linguistic stimuli across multiple views is still a critical open question in neuroscience. Consider three different views of the concept apartment: (1) picture (WP) presented with the target word label, (2) sentence (S) using the target w... |
Title: Predictive Accuracy of a Hybrid Generalized Long Memory Model for Short Term Electricity Price Forecasting Abstract: Accurate electricity price forecasting is the main management goal for market participants since it represents the fundamental basis to maximize the profits for market players. However, electricit... |
Title: Energy-Efficient Tree-Based EEG Artifact Detection Abstract: In the context of epilepsy monitoring, EEG artifacts are often mistaken for seizures due to their morphological similarity in both amplitude and frequency, making seizure detection systems susceptible to higher false alarm rates. In this work we presen... |
Title: Restructuring TCAD System: Teaching Traditional TCAD New Tricks Abstract: Traditional TCAD simulation has succeeded in predicting and optimizing the device performance; however, it still faces a massive challenge - a high computational cost. There have been many attempts to replace TCAD with deep learning, but i... |
Title: A Survey and Perspective on Artificial Intelligence for Security-Aware Electronic Design Automation Abstract: Artificial intelligence (AI) and machine learning (ML) techniques have been increasingly used in several fields to improve performance and the level of automation. In recent years, this use has exponenti... |
Title: Improved Worst-Group Robustness via Classifier Retraining on Independent Splits Abstract: High-capacity deep neural networks (DNNs) trained with Empirical Risk Minimization (ERM) often suffer from poor worst-group accuracy despite good on-average performance, where worst-group accuracy measures a model's robustn... |
Title: Exploring Descriptions of Movement Through Geovisual Analytics Abstract: Sensemaking using automatically extracted information from text is a challenging problem. In this paper, we address a specific type of information extraction, namely extracting information related to descriptions of movement. Aggregating an... |
Title: Distantly Supervised Named Entity Recognition via Confidence-Based Multi-Class Positive and Unlabeled Learning Abstract: In this paper, we study the named entity recognition (NER) problem under distant supervision. Due to the incompleteness of the external dictionaries and/or knowledge bases, such distantly anno... |
Title: Predicting Clinical Intent from Free Text Electronic Health Records Abstract: After a patient consultation, a clinician determines the steps in the management of the patient. A clinician may for example request to see the patient again or refer them to a specialist. Whilst most clinicians will record their inten... |
Title: Deep Reinforcement Learning for a Two-Echelon Supply Chain with Seasonal Demand Abstract: This paper leverages recent developments in reinforcement learning and deep learning to solve the supply chain inventory management problem, a complex sequential decision-making problem consisting of determining the optimal... |
Title: Detecting Unintended Memorization in Language-Model-Fused ASR Abstract: End-to-end (E2E) models are often being accompanied by language models (LMs) via shallow fusion for boosting their overall quality as well as recognition of rare words. At the same time, several prior works show that LMs are susceptible to u... |
Title: Assembly Planning from Observations under Physical Constraints Abstract: This paper addresses the problem of copying an unknown assembly of primitives with known shape and appearance using information extracted from a single photograph by an off-the-shelf procedure for object detection and pose estimation. The p... |
Title: Estimating city-wide hourly bicycle flow using a hybrid LSTM MDN Abstract: Cycling can reduce greenhouse gas emissions and air pollution and increase public health. With this in mind, policy-makers in cities worldwide seek to improve the bicycle mode-share. However, they often struggle against the fear and the p... |
Title: A Brief Guide to Designing and Evaluating Human-Centered Interactive Machine Learning Abstract: Interactive machine learning (IML) is a field of research that explores how to leverage both human and computational abilities in decision making systems. IML represents a collaboration between multiple complementary ... |
Title: SurvLatent ODE : A Neural ODE based time-to-event model with competing risks for longitudinal data improves cancer-associated Deep Vein Thrombosis (DVT) prediction Abstract: Effective learning from electronic health records (EHR) data for prediction of clinical outcomes is often challenging because of features r... |
Title: Clotho-AQA: A Crowdsourced Dataset for Audio Question Answering Abstract: Audio question answering (AQA) is a multimodal translation task where a system analyzes an audio signal and a natural language question, to generate a desirable natural language answer. In this paper, we introduce Clotho-AQA, a dataset for... |
Title: An Interpretable Probabilistic Autoregressive Neural Network Model for Time Series Forecasting Abstract: Forecasting time series data presents an emerging field of data science that has its application ranging from stock price and exchange rate prediction to the early prediction of epidemics. Numerous statistica... |
Title: The TalkMoves Dataset: K-12 Mathematics Lesson Transcripts Annotated for Teacher and Student Discursive Moves Abstract: Transcripts of teaching episodes can be effective tools to understand discourse patterns in classroom instruction. According to most educational experts, sustained classroom discourse is a crit... |
Title: A Fast Post-Training Pruning Framework for Transformers Abstract: Pruning is an effective way to reduce the huge inference cost of large Transformer models. However, prior work on model pruning requires retraining the model. This can add high cost and complexity to model deployment, making it difficult to use in... |
Title: Generative Design Ideation: A Natural Language Generation Approach Abstract: This paper aims to explore a generative approach for knowledge-based design ideation by applying the latest pre-trained language models in artificial intelligence (AI). Specifically, a method of fine-tuning the generative pre-trained tr... |
Title: Deep Learning meets Nonparametric Regression: Are Weight-Decayed DNNs Locally Adaptive? Abstract: We study the theory of neural network (NN) from the lens of classical nonparametric regression problems with a focus on NN's ability to adaptively estimate functions with heterogeneous smoothness -- a property of fu... |
Title: FS-NCSR: Increasing Diversity of the Super-Resolution Space via Frequency Separation and Noise-Conditioned Normalizing Flow Abstract: Super-resolution suffers from an innate ill-posed problem that a single low-resolution (LR) image can be from multiple high-resolution (HR) images. Recent studies on the flow-base... |
Title: Generative Pre-Trained Transformers for Biologically Inspired Design Abstract: Biological systems in nature have evolved for millions of years to adapt and survive the environment. Many features they developed can be inspirational and beneficial for solving technical problems in modern industries. This leads to ... |
Title: Scaling Language Model Size in Cross-Device Federated Learning Abstract: Most studies in cross-device federated learning focus on small models, due to the server-client communication and on-device computation bottlenecks. In this work, we leverage various techniques for mitigating these bottlenecks to train larg... |
Title: Matching Writers to Content Writing Tasks Abstract: Businesses need content. In various forms and formats and for varied purposes. In fact, the content marketing industry is set to be worth $412.88 billion by the end of 2021. However, according to the Content Marketing Institute, creating engaging content is the... |
Title: A majorization-minimization algorithm for nonnegative binary matrix factorization Abstract: This paper tackles the problem of decomposing binary data using matrix factorization. We consider the family of mean-parametrized Bernoulli models, a class of generative models that are well suited for modeling binary dat... |
Title: Federated Learning for Energy-limited Wireless Networks: A Partial Model Aggregation Approach Abstract: The limited communication resources, e.g., bandwidth and energy, and data heterogeneity across devices are two of the main bottlenecks for federated learning (FL). To tackle these challenges, we first devise a... |
Title: A Hierarchical Bayesian Approach to Inverse Reinforcement Learning with Symbolic Reward Machines Abstract: A misspecified reward can degrade sample efficiency and induce undesired behaviors in reinforcement learning (RL) problems. We propose symbolic reward machines for incorporating high-level task knowledge wh... |
Title: Multi-label classification for biomedical literature: an overview of the BioCreative VII LitCovid Track for COVID-19 literature topic annotations Abstract: The COVID-19 pandemic has been severely impacting global society since December 2019. Massive research has been undertaken to understand the characteristics ... |
Title: Reinforcement Learning from Partial Observation: Linear Function Approximation with Provable Sample Efficiency Abstract: We study reinforcement learning for partially observed Markov decision processes (POMDPs) with infinite observation and state spaces, which remains less investigated theoretically. To this end... |
Title: Wrapped Distributions on homogeneous Riemannian manifolds Abstract: We provide a general framework for constructing probability distributions on Riemannian manifolds, taking advantage of area-preserving maps and isometries. Control over distributions' properties, such as parameters, symmetry and modality yield a... |
Title: Assessing Machine Learning Algorithms for Near-Real Time Bus Ridership Prediction During Extreme Weather Abstract: Given an increasingly volatile climate, the relationship between weather and transit ridership has drawn increasing interest. However, challenges stemming from spatio-temporal dependency and non-sta... |
Title: Exact Formulas for Finite-Time Estimation Errors of Decentralized Temporal Difference Learning with Linear Function Approximation Abstract: In this paper, we consider the policy evaluation problem in multi-agent reinforcement learning (MARL) and derive exact closed-form formulas for the finite-time mean-squared ... |
Title: GUARD: Graph Universal Adversarial Defense Abstract: Graph convolutional networks (GCNs) have shown to be vulnerable to small adversarial perturbations, which becomes a severe threat and largely limits their applications in security-critical scenarios. To mitigate such a threat, considerable research efforts hav... |
Title: fairDMS: Rapid Model Training by Data and Model Reuse Abstract: Extracting actionable information from data sources such as the Linac Coherent Light Source (LCLS-II) and Advanced Photon Source Upgrade (APS-U) is becoming more challenging due to the fast-growing data generation rate. The rapid analysis possible w... |
Title: Deep transfer learning for partial differential equations under conditional shift with DeepONet Abstract: Traditional machine learning algorithms are designed to learn in isolation, i.e. address single tasks. The core idea of transfer learning (TL) is that knowledge gained in learning to perform one task (source... |
Title: A Revealing Large-Scale Evaluation of Unsupervised Anomaly Detection Algorithms Abstract: Anomaly detection has many applications ranging from bank-fraud detection and cyber-threat detection to equipment maintenance and health monitoring. However, choosing a suitable algorithm for a given application remains a c... |
Title: Relevance-guided Unsupervised Discovery of Abilities with Quality-Diversity Algorithms Abstract: Quality-Diversity algorithms provide efficient mechanisms to generate large collections of diverse and high-performing solutions, which have shown to be instrumental for solving downstream tasks. However, most of tho... |
Title: Accurate Molecular-Orbital-Based Machine Learning Energies via Unsupervised Clustering of Chemical Space Abstract: We introduce an unsupervised clustering algorithm to improve training efficiency and accuracy in predicting energies using molecular-orbital-based machine learning (MOB-ML). This work determines clu... |
Title: Memory Bounds for the Experts Problem Abstract: Online learning with expert advice is a fundamental problem of sequential prediction. In this problem, the algorithm has access to a set of $n$ "experts" who make predictions on each day. The goal on each day is to process these predictions, and make a prediction w... |
Title: Multi-Tier Platform for Cognizing Massive Electroencephalogram Abstract: An end-to-end platform assembling multiple tiers is built for precisely cognizing brain activities. Being fed massive electroencephalogram (EEG) data, the time-frequency spectrograms are conventionally projected into the episode-wise featur... |
Title: FedCL: Federated Contrastive Learning for Privacy-Preserving Recommendation Abstract: Contrastive learning is widely used for recommendation model learning, where selecting representative and informative negative samples is critical. Existing methods usually focus on centralized data, where abundant and high-qua... |
Title: CNLL: A Semi-supervised Approach For Continual Noisy Label Learning Abstract: The task of continual learning requires careful design of algorithms that can tackle catastrophic forgetting. However, the noisy label, which is inevitable in a real-world scenario, seems to exacerbate the situation. While very few stu... |
Title: Fairness in Graph Mining: A Survey Abstract: Graph mining algorithms have been playing a significant role in myriad fields over the years. However, despite their promising performance on various graph analytical tasks, most of these algorithms lack fairness considerations. As a consequence, they could lead to di... |
Title: Inducing Gaussian Process Networks Abstract: Gaussian processes (GPs) are powerful but computationally expensive machine learning models, requiring an estimate of the kernel covariance matrix for every prediction. In large and complex domains, such as graphs, sets, or images, the choice of suitable kernel can al... |
Title: Infographics Wizard: Flexible Infographics Authoring and Design Exploration Abstract: Infographics are an aesthetic visual representation of information following specific design principles of human perception. Designing infographics can be a tedious process for non-experts and time-consuming, even for professio... |
Title: MRAM-based Analog Sigmoid Function for In-memory Computing Abstract: We propose an analog implementation of the transcendental activation function leveraging two spin-orbit torque magnetoresistive random-access memory (SOT-MRAM) devices and a CMOS inverter. The proposed analog neuron circuit consumes 1.8-27x les... |
Title: Perception Visualization: Seeing Through the Eyes of a DNN Abstract: Artificial intelligence (AI) systems power the world we live in. Deep neural networks (DNNs) are able to solve tasks in an ever-expanding landscape of scenarios, but our eagerness to apply these powerful models leads us to focus on their perfor... |
Title: FastDiff: A Fast Conditional Diffusion Model for High-Quality Speech Synthesis Abstract: Denoising diffusion probabilistic models (DDPMs) have recently achieved leading performances in many generative tasks. However, the inherited iterative sampling process costs hindered their applications to speech synthesis. ... |
Title: Ultra-marginal Feature Importance Abstract: Scientists frequently prioritize learning from data rather than training the best possible model; however, research in machine learning often prioritizes the latter. Marginal feature importance methods, such as marginal contribution feature importance (MCI), attempt to... |
Title: Hybrid Cloud-Edge Collaborative Data Anomaly Detection in Industrial Sensor Networks Abstract: Industrial control systems (ICSs) are facing increasing cyber-physical attacks that can cause catastrophes in the physical system. Efficient anomaly detection models in the industrial sensor networks are essential for ... |
Title: Towards Reliable Neural Generative Modeling of Detectors Abstract: The increasing luminosities of future data taking at Large Hadron Collider and next generation collider experiments require an unprecedented amount of simulated events to be produced. Such large scale productions demand a significant amount of va... |
Title: Merging of neural networks Abstract: We propose a simple scheme for merging two neural networks trained with different starting initialization into a single one with the same size as the original ones. We do this by carefully selecting channels from each input network. Our procedure might be used as a finalizati... |
Title: Eliminating Backdoor Triggers for Deep Neural Networks Using Attention Relation Graph Distillation Abstract: Due to the prosperity of Artificial Intelligence (AI) techniques, more and more backdoors are designed by adversaries to attack Deep Neural Networks (DNNs).Although the state-of-the-art method Neural Atte... |
Title: A data filling methodology for time series based on CNN and (Bi)LSTM neural networks Abstract: In the process of collecting data from sensors, several circumstances can affect their continuity and validity, resulting in alterations of the data or loss of information. Although classical methods of statistics, suc... |
Title: Fluctuation-based Outlier Detection Abstract: Outlier detection is an important topic in machine learning and has been used in a wide range of applications. Outliers are objects that are few in number and deviate from the majority of objects. As a result of these two properties, we show that outliers are suscept... |
Title: MedFACT: Modeling Medical Feature Correlations in Patient Health Representation Learning via Feature Clustering Abstract: In healthcare prediction tasks, it is essential to exploit the correlations between medical features and learn better patient health representations. Existing methods try to estimate feature ... |
Title: Cross-Speaker Emotion Transfer for Low-Resource Text-to-Speech Using Non-Parallel Voice Conversion with Pitch-Shift Data Augmentation Abstract: Data augmentation via voice conversion (VC) has been successfully applied to low-resource expressive text-to-speech (TTS) when only neutral data for the target speaker a... |
Title: Scalable Sensitivity and Uncertainty Analysis for Causal-Effect Estimates of Continuous-Valued Interventions Abstract: Estimating the effects of continuous-valued interventions from observational data is a critically important task for climate science, healthcare, and economics. Recent work focuses on designing ... |
Title: Understanding the Domain Gap in LiDAR Object Detection Networks Abstract: In order to make autonomous driving a reality, artificial neural networks have to work reliably in the open-world. However, the open-world is vast and continuously changing, so it is not technically feasible to collect and annotate trainin... |
Title: Is Neuron Coverage Needed to Make Person Detection More Robust? Abstract: The growing use of deep neural networks (DNNs) in safety- and security-critical areas like autonomous driving raises the need for their systematic testing. Coverage-guided testing (CGT) is an approach that applies mutation or fuzzing accor... |
Title: A Learned Index for Exact Similarity Search in Metric Spaces Abstract: Indexing is an effective way to support efficient query processing in large databases. Recently the concept of learned index has been explored actively to replace or supplement traditional index structures with machine learning models to redu... |
Title: DropMessage: Unifying Random Dropping for Graph Neural Networks Abstract: Graph Neural Networks (GNNs) are powerful tools for graph representation learning. Despite their rapid development, GNNs also faces some challenges, such as over-fitting, over-smoothing, and non-robustness. Previous works indicate that the... |
Title: Robustness of Machine Learning Models Beyond Adversarial Attacks Abstract: Correctly quantifying the robustness of machine learning models is a central aspect in judging their suitability for specific tasks, and thus, ultimately, for generating trust in the models. We show that the widely used concept of adversa... |
Title: On Distribution Shift in Learning-based Bug Detectors Abstract: Deep learning has recently achieved initial success in program analysis tasks such as bug detection. Lacking real bugs, most existing works construct training and test data by injecting synthetic bugs into correct programs. Despite achieving high te... |
Title: Detecting Topology Attacks against Graph Neural Networks Abstract: Graph neural networks (GNNs) have been widely used in many real applications, and recent studies have revealed their vulnerabilities against topology attacks. To address this issue, existing efforts have mainly been dedicated to improving the rob... |
Title: A two-level machine learning framework for predictive maintenance: comparison of learning formulations Abstract: Predicting incoming failures and scheduling maintenance based on sensors information in industrial machines is increasingly important to avoid downtime and machine failure. Different machine learning ... |
Title: Working memory inspired hierarchical video decomposition with transformative representations Abstract: Video decomposition is very important to extract moving foreground objects from complex backgrounds in computer vision, machine learning, and medical imaging, e.g., extracting moving contrast-filled vessels fro... |
Title: Physical Modeling using Recurrent Neural Networks with Fast Convolutional Layers Abstract: Discrete-time modeling of acoustic, mechanical and electrical systems is a prominent topic in the musical signal processing literature. Such models are mostly derived by discretizing a mathematical model, given in terms of... |
Title: Learnable Model Augmentation Self-Supervised Learning for Sequential Recommendation Abstract: Sequential Recommendation aims to predict the next item based on user behaviour. Recently, Self-Supervised Learning (SSL) has been proposed to improve recommendation performance. However, most of existing SSL methods us... |
Title: Automated analysis of fibrous cap in intravascular optical coherence tomography images of coronary arteries Abstract: Thin-cap fibroatheroma (TCFA) and plaque rupture have been recognized as the most frequent risk factor for thrombosis and acute coronary syndrome. Intravascular optical coherence tomography (IVOC... |
Title: Evolution and use of data science vocabulary. How much have we changed in 13 years? Abstract: Here I present an investigation on the evolution and use of vocabulary in data science in the last 13 years. Based on a rigorous statistical analysis, a database with 12,787 documents containing the words "data science"... |
Title: Scale Dependencies and Self-Similarity Through Wavelet Scattering Covariance Abstract: We introduce a scattering covariance matrix which provides non-Gaussian models of time-series having stationary increments. A complex wavelet transform computes signal variations at each scale. Dependencies across scales are c... |
Title: Distributed Learning for Vehicular Dynamic Spectrum Access in Autonomous Driving Abstract: Reliable wireless communication between the autonomously driving cars is one of the fundamental needs for guaranteeing passenger safety and comfort. However, when the number of communicating cars increases, the transmissio... |
Title: Multi-Component Optimization and Efficient Deployment of Neural-Networks on Resource-Constrained IoT Hardware Abstract: The majority of IoT devices like smartwatches, smart plugs, HVAC controllers, etc., are powered by hardware with a constrained specification (low memory, clock speed and processor) which is ins... |
Title: INSPIRE: Distributed Bayesian Optimization for ImproviNg SPatIal REuse in Dense WLANs Abstract: WLANs, which have overtaken wired networks to become the primary means of connecting devices to the Internet, are prone to performance issues due to the scarcity of space in the radio spectrum. As a response, IEEE 802... |
Title: Social Media Sentiment Analysis for Cryptocurrency Market Prediction Abstract: In this paper, we explore the usability of different natural language processing models for the sentiment analysis of social media applied to financial market prediction, using the cryptocurrency domain as a reference. We study how th... |
Title: Neural Topic Modeling of Psychotherapy Sessions Abstract: In this work, we compare different neural topic modeling methods in learning the topical propensities of different psychiatric conditions from the psychotherapy session transcripts parsed from speech recordings. We also incorporate temporal modeling to pu... |
Title: Condition Monitoring of Transformer Bushings Using Computational Intelligence Abstract: Dissolved Gas-in-oil analysis (DGA) is used to monitor the condition of bushings on large power transformers. There are different techniques used in determining the conditions from the data collected, but in this work the Art... |
Title: IIITDWD-ShankarB@ Dravidian-CodeMixi-HASOC2021: mBERT based model for identification of offensive content in south Indian languages Abstract: In recent years, there has been a lot of focus on offensive content. The amount of offensive content generated by social media is increasing at an alarming rate. This crea... |
Title: Unsupervised Numerical Reasoning to Extract Phenotypes from Clinical Text by Leveraging External Knowledge Abstract: Extracting phenotypes from clinical text has been shown to be useful for a variety of clinical use cases such as identifying patients with rare diseases. However, reasoning with numerical values r... |
Title: BTranspose: Bottleneck Transformers for Human Pose Estimation with Self-Supervised Pre-Training Abstract: The task of 2D human pose estimation is challenging as the number of keypoints is typically large (~ 17) and this necessitates the use of robust neural network architectures and training pipelines that can c... |
Title: OCTOPUS -- optical coherence tomography plaque and stent analysis software Abstract: Compared with other imaging modalities, intravascular optical coherence tomography (IVOCT) has significant advantages for guiding percutaneous coronary interventions. To aid IVOCT research studies, we developed the Optical Coher... |
Title: Learning spatiotemporal features from incomplete data for traffic flow prediction using hybrid deep neural networks Abstract: Urban traffic flow prediction using data-driven models can play an important role in route planning and preventing congestion on highways. These methods utilize data collected from traffi... |
Title: The Silent Problem -- Machine Learning Model Failure -- How to Diagnose and Fix Ailing Machine Learning Models Abstract: The COVID-19 pandemic has dramatically changed how healthcare is delivered to patients, how patients interact with healthcare providers, and how healthcare information is disseminated to both ... |
Title: The NIST CTS Speaker Recognition Challenge Abstract: The US National Institute of Standards and Technology (NIST) has been conducting a second iteration of the CTS challenge since August 2020. The current iteration of the CTS Challenge is a leaderboard-style speaker recognition evaluation using telephony data ex... |
Title: Handling Imbalanced Classification Problems With Support Vector Machines via Evolutionary Bilevel Optimization Abstract: Support vector machines (SVMs) are popular learning algorithms to deal with binary classification problems. They traditionally assume equal misclassification costs for each class; however, rea... |
Title: A Sandbox Tool to Bias(Stress)-Test Fairness Algorithms Abstract: Motivated by the growing importance of reducing unfairness in ML predictions, Fair-ML researchers have presented an extensive suite of algorithmic "fairness-enhancing" remedies. Most existing algorithms, however, are agnostic to the sources of the... |
Title: The 2021 NIST Speaker Recognition Evaluation Abstract: The 2021 Speaker Recognition Evaluation (SRE21) was the latest cycle of the ongoing evaluation series conducted by the U.S. National Institute of Standards and Technology (NIST) since 1996. It was the second large-scale multimodal speaker/person recognition ... |
Title: Revisiting Gaussian mixture critics in off-policy reinforcement learning: a sample-based approach Abstract: Actor-critic algorithms that make use of distributional policy evaluation have frequently been shown to outperform their non-distributional counterparts on many challenging control tasks. Examples of this ... |
Title: DooDLeNet: Double DeepLab Enhanced Feature Fusion for Thermal-color Semantic Segmentation Abstract: In this paper we present a new approach for feature fusion between RGB and LWIR Thermal images for the task of semantic segmentation for driving perception. We propose DooDLeNet, a double DeepLab architecture with... |
Title: Out-of-distribution generalization for learning quantum dynamics Abstract: Generalization bounds are a critical tool to assess the training data requirements of Quantum Machine Learning (QML). Recent work has established guarantees for in-distribution generalization of quantum neural networks (QNNs), where train... |
Title: Dynamical simulation via quantum machine learning with provable generalization Abstract: Much attention has been paid to dynamical simulation and quantum machine learning (QML) independently as applications for quantum advantage, while the possibility of using QML to enhance dynamical simulations has not been th... |
Title: Deep learning techniques for energy clustering in the CMS ECAL Abstract: The reconstruction of electrons and photons in CMS depends on topological clustering of the energy deposited by an incident particle in different crystals of the electromagnetic calorimeter (ECAL). These clusters are formed by aggregating n... |
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