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Title: Cooperative Behavioral Planning for Automated Driving using Graph Neural Networks Abstract: Urban intersections are prone to delays and inefficiencies due to static precedence rules and occlusions limiting the view on prioritized traffic. Existing approaches to improve traffic flow, widely known as automatic int... |
Title: Deep Metric Learning-Based Semi-Supervised Regression With Alternate Learning Abstract: This paper introduces a novel deep metric learning-based semi-supervised regression (DML-S2R) method for parameter estimation problems. The proposed DML-S2R method aims to mitigate the problems of insufficient amount of label... |
Title: Fast Sparse Classification for Generalized Linear and Additive Models Abstract: We present fast classification techniques for sparse generalized linear and additive models. These techniques can handle thousands of features and thousands of observations in minutes, even in the presence of many highly correlated f... |
Title: A Differential Attention Fusion Model Based on Transformer for Time Series Forecasting Abstract: Time series forecasting is widely used in the fields of equipment life cycle forecasting, weather forecasting, traffic flow forecasting, and other fields. Recently, some scholars have tried to apply Transformer to ti... |
Title: Privacy issues on biometric systems Abstract: In the XXIth century there is a strong interest on privacy issues. Technology permits obtaining personal information without individuals consent, computers make it feasible to share and process this information, and this can bring about damaging implications. In some... |
Title: Testing report of a fingerprint-based door-opening system Abstract: This paper describes the operational evaluation of a door-opening system based on a low-cost inkless fingerprint sensor. This system has been developed and installed for access control to one of our laboratories. Experimental results reveal that... |
Title: Towards Speaker Age Estimation with Label Distribution Learning Abstract: Existing methods for speaker age estimation usually treat it as a multi-class classification or a regression problem. However, precise age identification remains a challenge due to label ambiguity, \emph{i.e.}, utterances from adjacent age... |
Title: Extension of Dynamic Mode Decomposition for dynamic systems with incomplete information based on t-model of optimal prediction Abstract: The Dynamic Mode Decomposition has proved to be a very efficient technique to study dynamic data. This is entirely a data-driven approach that extracts all necessary informatio... |
Title: Enabling arbitrary translation objectives with Adaptive Tree Search Abstract: We introduce an adaptive tree search algorithm, that can find high-scoring outputs under translation models that make no assumptions about the form or structure of the search objective. This algorithm -- a deterministic variant of Mont... |
Title: Reconstruction of observed mechanical motions with Artificial Intelligence tools Abstract: The goal of this paper is to determine the laws of observed trajectories assuming that there is a mechanical system in the background and using these laws to continue the observed motion in a plausible way. The laws are re... |
Title: Deep Learning Reproducibility and Explainable AI (XAI) Abstract: The nondeterminism of Deep Learning (DL) training algorithms and its influence on the explainability of neural network (NN) models are investigated in this work with the help of image classification examples. To discuss the issue, two convolutional... |
Title: Bitwidth Heterogeneous Federated Learning with Progressive Weight Dequantization Abstract: In practical federated learning scenarios, the participating devices may have different bitwidths for computation and memory storage by design. However, despite the progress made in device-heterogeneous federated learning ... |
Title: On PAC-Bayesian reconstruction guarantees for VAEs Abstract: Despite its wide use and empirical successes, the theoretical understanding and study of the behaviour and performance of the variational autoencoder (VAE) have only emerged in the past few years. We contribute to this recent line of work by analysing ... |
Title: Biometric security technology Abstract: This paper presents an overview of the main topics related to biometric security technology, with the main purpose to provide a primer on this subject. Biometrics can offer greater security and convenience than traditional methods for people recognition. Even if we do not ... |
Title: Exponential Tail Local Rademacher Complexity Risk Bounds Without the Bernstein Condition Abstract: The local Rademacher complexity framework is one of the most successful general-purpose toolboxes for establishing sharp excess risk bounds for statistical estimators based on the framework of empirical risk minimi... |
Title: Thermal hand image segmentation for biometric recognition Abstract: In this paper we present a method to identify people by means of thermal (TH) and visible (VIS) hand images acquired simultaneously with a TESTO 882-3 camera. In addition, we also present a new database specially acquired for this work. The real... |
Title: Residual Bootstrap Exploration for Stochastic Linear Bandit Abstract: We propose a new bootstrap-based online algorithm for stochastic linear bandit problems. The key idea is to adopt residual bootstrap exploration, in which the agent estimates the next step reward by re-sampling the residuals of mean reward est... |
Title: Listen to Interpret: Post-hoc Interpretability for Audio Networks with NMF Abstract: This paper tackles post-hoc interpretability for audio processing networks. Our goal is to interpret decisions of a network in terms of high-level audio objects that are also listenable for the end-user. To this end, we propose ... |
Title: Learning Temporal Point Processes for Efficient Retrieval of Continuous Time Event Sequences Abstract: Recent developments in predictive modeling using marked temporal point processes (MTPP) have enabled an accurate characterization of several real-world applications involving continuous-time event sequences (CT... |
Title: Augmentation based unsupervised domain adaptation Abstract: The insertion of deep learning in medical image analysis had lead to the development of state-of-the art strategies in several applications such a disease classification, as well as abnormality detection and segmentation. However, even the most advanced... |
Title: Towards Tailored Models on Private AIoT Devices: Federated Direct Neural Architecture Search Abstract: Neural networks often encounter various stringent resource constraints while deploying on edge devices. To tackle these problems with less human efforts, automated machine learning becomes popular in finding va... |
Title: Networked Online Learning for Control of Safety-Critical Resource-Constrained Systems based on Gaussian Processes Abstract: Safety-critical technical systems operating in unknown environments require the ability to quickly adapt their behavior, which can be achieved in control by inferring a model online from th... |
Title: Fairness-Aware Naive Bayes Classifier for Data with Multiple Sensitive Features Abstract: Fairness-aware machine learning seeks to maximise utility in generating predictions while avoiding unfair discrimination based on sensitive attributes such as race, sex, religion, etc. An important line of work in this fiel... |
Title: Using Deep Reinforcement Learning with Automatic Curriculum Learning for Mapless Navigation in Intralogistics Abstract: We propose a deep reinforcement learning approach for solving a mapless navigation problem in warehouse scenarios. In our approach, an automation guided vehicle is equipped with LiDAR and front... |
Title: A Comparative Study of Deep Reinforcement Learning-based Transferable Energy Management Strategies for Hybrid Electric Vehicles Abstract: The deep reinforcement learning-based energy management strategies (EMS) have become a promising solution for hybrid electric vehicles (HEVs). When driving cycles are changed,... |
Title: Non-Volatile Memory Accelerated Geometric Multi-Scale Resolution Analysis Abstract: Dimensionality reduction algorithms are standard tools in a researcher's toolbox. Dimensionality reduction algorithms are frequently used to augment downstream tasks such as machine learning, data science, and also are explorator... |
Title: GIFT: Graph-guIded Feature Transfer for Cold-Start Video Click-Through Rate Prediction Abstract: Short video has witnessed rapid growth in China and shows a promising market for promoting the sales of products in e-commerce platforms like Taobao. To ensure the freshness of the content, the platform needs to rele... |
Title: A new LDA formulation with covariates Abstract: The Latent Dirichlet Allocation (LDA) model is a popular method for creating mixed-membership clusters. Despite having been originally developed for text analysis, LDA has been used for a wide range of other applications. We propose a new formulation for the LDA mo... |
Title: Diffractive optical system design by cascaded propagation Abstract: Modern design of complex optical systems relies heavily on computational tools. These typically utilize geometrical optics as well as Fourier optics, which enables the use of diffractive elements to manipulate light with features on the scale of... |
Title: Robust Geometric Metric Learning Abstract: This paper proposes new algorithms for the metric learning problem. We start by noticing that several classical metric learning formulations from the literature can be viewed as modified covariance matrix estimation problems. Leveraging this point of view, a general app... |
Title: Short-answer scoring with ensembles of pretrained language models Abstract: We investigate the effectiveness of ensembles of pretrained transformer-based language models on short answer questions using the Kaggle Automated Short Answer Scoring dataset. We fine-tune a collection of popular small, base, and large ... |
Title: Pessimistic Bootstrapping for Uncertainty-Driven Offline Reinforcement Learning Abstract: Offline Reinforcement Learning (RL) aims to learn policies from previously collected datasets without exploring the environment. Directly applying off-policy algorithms to offline RL usually fails due to the extrapolation e... |
Title: Shisha: Online scheduling of CNN pipelines on heterogeneous architectures Abstract: Chiplets have become a common methodology in modern chip design. Chiplets improve yield and enable heterogeneity at the level of cores, memory subsystem and the interconnect. Convolutional Neural Networks (CNNs) have high computa... |
Title: Amortised Likelihood-free Inference for Expensive Time-series Simulators with Signatured Ratio Estimation Abstract: Simulation models of complex dynamics in the natural and social sciences commonly lack a tractable likelihood function, rendering traditional likelihood-based statistical inference impossible. Rece... |
Title: How Many Data Are Needed for Robust Learning? Abstract: We show that the sample complexity of robust interpolation problem could be exponential in the input dimensionality and discover a phase transition phenomenon when the data are in a unit ball. Robust interpolation refers to the problem of interpolating $n$ ... |
Title: Finding Safe Zones of policies Markov Decision Processes Abstract: Given a policy, we define a SafeZone as a subset of states, such that most of the policy's trajectories are confined to this subset. The quality of the SafeZone is parameterized by the number of states and the escape probability, i.e., the probab... |
Title: A Dimensionality Reduction Method for Finding Least Favorable Priors with a Focus on Bregman Divergence Abstract: A common way of characterizing minimax estimators in point estimation is by moving the problem into the Bayesian estimation domain and finding a least favorable prior distribution. The Bayesian estim... |
Title: Pricing options on flow forwards by neural networks in Hilbert space Abstract: We propose a new methodology for pricing options on flow forwards by applying infinite-dimensional neural networks. We recast the pricing problem as an optimization problem in a Hilbert space of real-valued function on the positive re... |
Title: Deep Bayesian ICP Covariance Estimation Abstract: Covariance estimation for the Iterative Closest Point (ICP) point cloud registration algorithm is essential for state estimation and sensor fusion purposes. We argue that a major source of error for ICP is in the input data itself, from the sensor noise to the sc... |
Title: Mirror Descent Strikes Again: Optimal Stochastic Convex Optimization under Infinite Noise Variance Abstract: We study stochastic convex optimization under infinite noise variance. Specifically, when the stochastic gradient is unbiased and has uniformly bounded $(1+\kappa)$-th moment, for some $\kappa \in (0,1]$,... |
Title: Cyclical Variational Bayes Monte Carlo for Efficient Multi-Modal Posterior Distributions Evaluation Abstract: Statistical model updating is frequently used in engineering to calculate the uncertainty of some unknown latent parameters when a set of measurements on observable quantities is given. Variational infer... |
Title: TEE-based decentralized recommender systems: The raw data sharing redemption Abstract: Recommenders are central in many applications today. The most effective recommendation schemes, such as those based on collaborative filtering (CF), exploit similarities between user profiles to make recommendations, but poten... |
Title: Globally Convergent Policy Search over Dynamic Filters for Output Estimation Abstract: We introduce the first direct policy search algorithm which provably converges to the globally optimal $\textit{dynamic}$ filter for the classical problem of predicting the outputs of a linear dynamical system, given noisy, pa... |
Title: Comparative analysis of machine learning methods for active flow control Abstract: Machine learning frameworks such as Genetic Programming (GP) and Reinforcement Learning (RL) are gaining popularity in flow control. This work presents a comparative analysis of the two, bench-marking some of their most representa... |
Title: Wide Mean-Field Bayesian Neural Networks Ignore the Data Abstract: Bayesian neural networks (BNNs) combine the expressive power of deep learning with the advantages of Bayesian formalism. In recent years, the analysis of wide, deep BNNs has provided theoretical insight into their priors and posteriors. However, ... |
Title: Learning Fast and Slow for Online Time Series Forecasting Abstract: The fast adaptation capability of deep neural networks in non-stationary environments is critical for online time series forecasting. Successful solutions require handling changes to new and recurring patterns. However, training deep neural fore... |
Title: Bayesian Model Selection, the Marginal Likelihood, and Generalization Abstract: How do we compare between hypotheses that are entirely consistent with observations? The marginal likelihood (aka Bayesian evidence), which represents the probability of generating our observations from a prior, provides a distinctiv... |
Title: MuMiN: A Large-Scale Multilingual Multimodal Fact-Checked Misinformation Social Network Dataset Abstract: Misinformation is becoming increasingly prevalent on social media and in news articles. It has become so widespread that we require algorithmic assistance utilising machine learning to detect such content. T... |
Title: A Class of Geometric Structures in Transfer Learning: Minimax Bounds and Optimality Abstract: We study the problem of transfer learning, observing that previous efforts to understand its information-theoretic limits do not fully exploit the geometric structure of the source and target domains. In contrast, our s... |
Title: Brain Structural Saliency Over The Ages Abstract: Brain Age (BA) estimation via Deep Learning has become a strong and reliable bio-marker for brain health, but the black-box nature of Neural Networks does not easily allow insight into the features of brain ageing. We trained a ResNet model as a BA regressor on T... |
Title: Super-resolution GANs of randomly-seeded fields Abstract: Reconstruction of field quantities from sparse measurements is a problem arising in a broad spectrum of applications. This task is particularly challenging when mapping between point sparse measurements and field quantities shall be performed in an unsupe... |
Title: COLD Decoding: Energy-based Constrained Text Generation with Langevin Dynamics Abstract: Many applications of text generation require incorporating different constraints to control the semantics or style of generated text. These constraints can be hard (e.g., ensuring certain keywords are included in the output)... |
Title: Flow-based sampling in the lattice Schwinger model at criticality Abstract: Recent results suggest that flow-based algorithms may provide efficient sampling of field distributions for lattice field theory applications, such as studies of quantum chromodynamics and the Schwinger model. In this work, we provide a ... |
Title: Completely Quantum Neural Networks Abstract: Artificial neural networks are at the heart of modern deep learning algorithms. We describe how to embed and train a general neural network in a quantum annealer without introducing any classical element in training. To implement the network on a state-of-the-art quan... |
Title: Using Bayesian Deep Learning to infer Planet Mass from Gaps in Protoplanetary Disks Abstract: Planet induced sub-structures, like annular gaps, observed in dust emission from protoplanetary disks provide a unique probe to characterize unseen young planets. While deep learning based model has an edge in character... |
Title: Truncated LinUCB for Stochastic Linear Bandits Abstract: This paper considers contextual bandits with a finite number of arms, where the contexts are independent and identically distributed $d$-dimensional random vectors, and the expected rewards are linear in both the arm parameters and contexts. The LinUCB alg... |
Title: The Need for Interpretable Features: Motivation and Taxonomy Abstract: Through extensive experience developing and explaining machine learning (ML) applications for real-world domains, we have learned that ML models are only as interpretable as their features. Even simple, highly interpretable model types such a... |
Title: Are All Linear Regions Created Equal? Abstract: The number of linear regions has been studied as a proxy of complexity for ReLU networks. However, the empirical success of network compression techniques like pruning and knowledge distillation, suggest that in the overparameterized setting, linear regions density... |
Title: Analysis of Coronavirus Envelope Protein with Cellular Automata (CA) Model Abstract: The reason of significantly higher transmissibility of SARS Covid (2019 CoV-2) compared to SARS Covid (2003 CoV) and MERS Covid (2012 MERS) can be attributed to mutations reported in structural proteins, and the role played by n... |
Title: ML-based Anomaly Detection in Optical Fiber Monitoring Abstract: Secure and reliable data communication in optical networks is critical for high-speed internet. We propose a data driven approach for the anomaly detection and faults identification in optical networks to diagnose physical attacks such as fiber bre... |
Title: Single Image Super-Resolution Methods: A Survey Abstract: Super-resolution (SR), the process of obtaining high-resolution images from one or more low-resolution observations of the same scene, has been a very popular topic of research in the last few decades in both signal processing and image processing areas. ... |
Title: When do GANs replicate? On the choice of dataset size Abstract: Do GANs replicate training images? Previous studies have shown that GANs do not seem to replicate training data without significant change in the training procedure. This leads to a series of research on the exact condition needed for GANs to overfi... |
Title: Discovering Multiple and Diverse Directions for Cognitive Image Properties Abstract: Recent research has shown that it is possible to find interpretable directions in the latent spaces of pre-trained GANs. These directions enable controllable generation and support a variety of semantic editing operations. While... |
Title: Prune and Tune Ensembles: Low-Cost Ensemble Learning With Sparse Independent Subnetworks Abstract: Ensemble Learning is an effective method for improving generalization in machine learning. However, as state-of-the-art neural networks grow larger, the computational cost associated with training several independe... |
Title: Adversarially-regularized mixed effects deep learning (ARMED) models for improved interpretability, performance, and generalization on clustered data Abstract: Natural science datasets frequently violate assumptions of independence. Samples may be clustered (e.g. by study site, subject, or experimental batch), l... |
Title: Generative modeling via tensor train sketching Abstract: In this paper we introduce a sketching algorithm for constructing a tensor train representation of a probability density from its samples. Our method deviates from the standard recursive SVD-based procedure for constructing a tensor train. Instead we formu... |
Title: Investigating the effect of binning on causal discovery Abstract: Binning (a.k.a. discretization) of numerically continuous measurements is a wide-spread but controversial practice in data collection, analysis, and presentation. The consequences of binning have been evaluated for many different kinds of data ana... |
Title: Exploiting Correlation to Achieve Faster Learning Rates in Low-Rank Preference Bandits Abstract: We introduce the \emph{Correlated Preference Bandits} problem with random utility-based choice models (RUMs), where the goal is to identify the best item from a given pool of $n$ items through online subsetwise prefe... |
Title: Training Characteristic Functions with Reinforcement Learning: XAI-methods play Connect Four Abstract: One of the goals of Explainable AI (XAI) is to determine which input components were relevant for a classifier decision. This is commonly know as saliency attribution. Characteristic functions (from cooperative... |
Title: Drawing Inductor Layout with a Reinforcement Learning Agent: Method and Application for VCO Inductors Abstract: Design of Voltage-Controlled Oscillator (VCO) inductors is a laborious and time-consuming task that is conventionally done manually by human experts. In this paper, we propose a framework for automatin... |
Title: NeuroView-RNN: It's About Time Abstract: Recurrent Neural Networks (RNNs) are important tools for processing sequential data such as time-series or video. Interpretability is defined as the ability to be understood by a person and is different from explainability, which is the ability to be explained in a mathem... |
Title: Benefit of Interpolation in Nearest Neighbor Algorithms Abstract: In some studies \citep[e.g.,][]{zhang2016understanding} of deep learning, it is observed that over-parametrized deep neural networks achieve a small testing error even when the training error is almost zero. Despite numerous works towards understa... |
Title: Consistent Dropout for Policy Gradient Reinforcement Learning Abstract: Dropout has long been a staple of supervised learning, but is rarely used in reinforcement learning. We analyze why naive application of dropout is problematic for policy-gradient learning algorithms and introduce consistent dropout, a simpl... |
Title: Nowcasting the Financial Time Series with Streaming Data Analytics under Apache Spark Abstract: This paper proposes nowcasting of high-frequency financial datasets in real-time with a 5-minute interval using the streaming analytics feature of Apache Spark. The proposed 2 stage method consists of modelling chaos ... |
Title: Physics-informed neural networks for inverse problems in supersonic flows Abstract: Accurate solutions to inverse supersonic compressible flow problems are often required for designing specialized aerospace vehicles. In particular, we consider the problem where we have data available for density gradients from S... |
Title: Differentially Private Speaker Anonymization Abstract: Sharing real-world speech utterances is key to the training and deployment of voice-based services. However, it also raises privacy risks as speech contains a wealth of personal data. Speaker anonymization aims to remove speaker information from a speech utt... |
Title: Using Deep Learning to Detect Digitally Encoded DNA Trigger for Trojan Malware in Bio-Cyber Attacks Abstract: This article uses Deep Learning technologies to safeguard DNA sequencing against Bio-Cyber attacks. We consider a hybrid attack scenario where the payload is encoded into a DNA sequence to activate a Tro... |
Title: A modification of the conjugate direction method for motion estimation Abstract: A comparative study of different block matching alternatives for motion estimation is presented. The study is focused on computational burden and objective measures on the accuracy of prediction. Together with existing algorithms se... |
Title: Sky Computing: Accelerating Geo-distributed Computing in Federated Learning Abstract: Federated learning is proposed by Google to safeguard data privacy through training models locally on users' devices. However, with deep learning models growing in size to achieve better results, it becomes increasingly difficu... |
Title: Explanatory Paradigms in Neural Networks Abstract: In this article, we present a leap-forward expansion to the study of explainability in neural networks by considering explanations as answers to abstract reasoning-based questions. With $P$ as the prediction from a neural network, these questions are `Why P?', `... |
Title: DC and SA: Robust and Efficient Hyperparameter Optimization of Multi-subnetwork Deep Learning Models Abstract: We present two novel hyperparameter optimization strategies for optimization of deep learning models with a modular architecture constructed of multiple subnetworks. As complex networks with multiple su... |
Title: First is Better Than Last for Training Data Influence Abstract: The ability to identify influential training examples enables us to debug training data and explain model behavior. Existing techniques to do so are based on the flow of training data influence through the model parameters. For large models in NLP a... |
Title: Robust Federated Learning with Connectivity Failures: A Semi-Decentralized Framework with Collaborative Relaying Abstract: Intermittent client connectivity is one of the major challenges in centralized federated edge learning frameworks. Intermittently failing uplinks to the central parameter server (PS) can ind... |
Title: Attainability and Optimality: The Equalized Odds Fairness Revisited Abstract: Fairness of machine learning algorithms has been of increasing interest. In order to suppress or eliminate discrimination in prediction, various notions as well as approaches have been proposed to impose fairness. Given a notion of fai... |
Title: Learning Multi-Object Dynamics with Compositional Neural Radiance Fields Abstract: We present a method to learn compositional predictive models from image observations based on implicit object encoders, Neural Radiance Fields (NeRFs), and graph neural networks. A central question in learning dynamic models from ... |
Title: Loss as the Inconsistency of a Probabilistic Dependency Graph: Choose Your Model, Not Your Loss Function Abstract: In a world blessed with a great diversity of loss functions, we argue that that choice between them is not a matter of taste or pragmatics, but of model. Probabilistic depencency graphs (PDGs) are p... |
Title: No-Regret Learning in Games is Turing Complete Abstract: Games are natural models for multi-agent machine learning settings, such as generative adversarial networks (GANs). The desirable outcomes from algorithmic interactions in these games are encoded as game theoretic equilibrium concepts, e.g. Nash and coarse... |
Title: A Unified Framework for Campaign Performance Forecasting in Online Display Advertising Abstract: Advertisers usually enjoy the flexibility to choose criteria like target audience, geographic area and bid price when planning an campaign for online display advertising, while they lack forecast information on campa... |
Title: A Note on Machine Learning Approach for Computational Imaging Abstract: Computational imaging has been playing a vital role in the development of natural sciences. Advances in sensory, information, and computer technologies have further extended the scope of influence of imaging, making digital images an essenti... |
Title: Controlling Memorability of Face Images Abstract: Everyday, we are bombarded with many photographs of faces, whether on social media, television, or smartphones. From an evolutionary perspective, faces are intended to be remembered, mainly due to survival and personal relevance. However, all these faces do not h... |
Title: An Efficient Binary Harris Hawks Optimization based on Quantum SVM for Cancer Classification Tasks Abstract: Cancer classification based on gene expression increases early diagnosis and recovery, but high-dimensional genes with a small number of samples are a major challenge. This work introduces a new hybrid qu... |
Title: Robust Probabilistic Time Series Forecasting Abstract: Probabilistic time series forecasting has played critical role in decision-making processes due to its capability to quantify uncertainties. Deep forecasting models, however, could be prone to input perturbations, and the notion of such perturbations, togeth... |
Title: A Rigorous Study of Integrated Gradients Method and Extensions to Internal Neuron Attributions Abstract: As the efficacy of deep learning (DL) grows, so do concerns about the lack of transparency of these black-box models. Attribution methods aim to improve transparency of DL models by quantifying an input featu... |
Title: Machine Learning for Intrusion Detection in Industrial Control Systems: Applications, Challenges, and Recommendations Abstract: Methods from machine learning are being applied to design Industrial Control Systems resilient to cyber-attacks. Such methods focus on two major areas: the detection of intrusions at th... |
Title: Threading the Needle of On and Off-Manifold Value Functions for Shapley Explanations Abstract: A popular explainable AI (XAI) approach to quantify feature importance of a given model is via Shapley values. These Shapley values arose in cooperative games, and hence a critical ingredient to compute these in an XAI... |
Title: Auto-scaling Vision Transformers without Training Abstract: This work targets automated designing and scaling of Vision Transformers (ViTs). The motivation comes from two pain spots: 1) the lack of efficient and principled methods for designing and scaling ViTs; 2) the tremendous computational cost of training V... |
Title: AutoCl : A Visual Interactive System for Automatic Deep Learning Classifier Recommendation Based on Models Performance Abstract: Nowadays, deep learning (DL) models being increasingly applied to various fields, people without technical expertise and domain knowledge struggle to find an appropriate model for thei... |
Title: On Learning Mixture Models with Sparse Parameters Abstract: Mixture models are widely used to fit complex and multimodal datasets. In this paper we study mixtures with high dimensional sparse latent parameter vectors and consider the problem of support recovery of those vectors. While parameter learning in mixtu... |
Title: XAutoML: A Visual Analytics Tool for Establishing Trust in Automated Machine Learning Abstract: In the last ten years, various automated machine learning (AutoML) systems have been proposed to build end-to-end machine learning (ML) pipelines with minimal human interaction. Even though such automatically synthesi... |
Title: All You Need Is Supervised Learning: From Imitation Learning to Meta-RL With Upside Down RL Abstract: Upside down reinforcement learning (UDRL) flips the conventional use of the return in the objective function in RL upside down, by taking returns as input and predicting actions. UDRL is based purely on supervis... |
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