text stringlengths 0 4.09k |
|---|
Title: An Empirical Study of Quantum Dynamics as a Ground State Problem with Neural Quantum States Abstract: Neural quantum states are variational wave functions parameterised by artificial neural networks, a mathematical model studied for decades in the machine learning community. In the context of many-body physics, ... |
Title: On the Role of Generalization in Transferability of Adversarial Examples Abstract: Black-box adversarial attacks designing adversarial examples for unseen neural networks (NNs) have received great attention over the past years. While several successful black-box attack schemes have been proposed in the literatur... |
Title: Model-Agnostic Few-Shot Open-Set Recognition Abstract: We tackle the Few-Shot Open-Set Recognition (FSOSR) problem, i.e. classifying instances among a set of classes for which we only have few labeled samples, while simultaneously detecting instances that do not belong to any known class. Departing from existing... |
Title: Bioinspired random projections for robust, sparse classification Abstract: Inspired by the use of random projections in biological sensing systems, we present a new algorithm for processing data in classification problems. This is based on observations of the human brain and the fruit fly's olfactory system and ... |
Title: An Invertible Graph Diffusion Neural Network for Source Localization Abstract: Localizing the source of graph diffusion phenomena, such as misinformation propagation, is an important yet extremely challenging task. Existing source localization models typically are heavily dependent on the hand-crafted rules. Unf... |
Title: Multi-Modality Image Inpainting using Generative Adversarial Networks Abstract: Deep learning techniques, especially Generative Adversarial Networks (GANs) have significantly improved image inpainting and image-to-image translation tasks over the past few years. To the best of our knowledge, the problem of combi... |
Title: EST: Evaluating Scientific Thinking in Artificial Agents Abstract: Theoretical ideas and empirical research have shown us a seemingly surprising result: children, even very young toddlers, demonstrate learning and thinking in a strikingly similar manner to scientific reasoning in formal research. Encountering a ... |
Title: EEML: Ensemble Embedded Meta-learning Abstract: To accelerate learning process with few samples, meta-learning resorts to prior knowledge from previous tasks. However, the inconsistent task distribution and heterogeneity is hard to be handled through a global sharing model initialization. In this paper, based on... |
Title: Efficient Aggregated Kernel Tests using Incomplete $U$-statistics Abstract: We propose a series of computationally efficient, nonparametric tests for the two-sample, independence and goodness-of-fit problems, using the Maximum Mean Discrepancy (MMD), Hilbert Schmidt Independence Criterion (HSIC), and Kernel Stei... |
Title: Multi-Modality Image Super-Resolution using Generative Adversarial Networks Abstract: Over the past few years deep learning-based techniques such as Generative Adversarial Networks (GANs) have significantly improved solutions to image super-resolution and image-to-image translation problems. In this paper, we pr... |
Title: Causal Inference with Treatment Measurement Error: A Nonparametric Instrumental Variable Approach Abstract: We propose a kernel-based nonparametric estimator for the causal effect when the cause is corrupted by error. We do so by generalizing estimation in the instrumental variable setting. Despite significant w... |
Title: PHN: Parallel heterogeneous network with soft gating for CTR prediction Abstract: The Click-though Rate (CTR) prediction task is a basic task in recommendation system. Most of the previous researches of CTR models built based on Wide \& deep structure and gradually evolved into parallel structures with different... |
Title: Coin Flipping Neural Networks Abstract: We show that neural networks with access to randomness can outperform deterministic networks by using amplification. We call such networks Coin-Flipping Neural Networks, or CFNNs. We show that a CFNN can approximate the indicator of a $d$-dimensional ball to arbitrary accu... |
Title: NAS-Bench-Graph: Benchmarking Graph Neural Architecture Search Abstract: Graph neural architecture search (GraphNAS) has recently aroused considerable attention in both academia and industry. However, two key challenges seriously hinder the further research of GraphNAS. First, since there is no consensus for the... |
Title: Thompson Sampling for (Combinatorial) Pure Exploration Abstract: Existing methods of combinatorial pure exploration mainly focus on the UCB approach. To make the algorithm efficient, they usually use the sum of upper confidence bounds within arm set $S$ to represent the upper confidence bound of $S$, which can b... |
Title: Piecewise Linear Neural Networks and Deep Learning Abstract: As a powerful modelling method, PieceWise Linear Neural Networks (PWLNNs) have proven successful in various fields, most recently in deep learning. To apply PWLNN methods, both the representation and the learning have long been studied. In 1977, the ca... |
Title: Beyond Real-world Benchmark Datasets: An Empirical Study of Node Classification with GNNs Abstract: Graph Neural Networks (GNNs) have achieved great success on a node classification task. Despite the broad interest in developing and evaluating GNNs, they have been assessed with limited benchmark datasets. As a r... |
Title: Certified Graph Unlearning Abstract: Graph-structured data is ubiquitous in practice and often processed using graph neural networks (GNNs). With the adoption of recent laws ensuring the ``right to be forgotten'', the problem of graph data removal has become of significant importance. To address the problem, we ... |
Title: Provable Generalization of Overparameterized Meta-learning Trained with SGD Abstract: Despite the superior empirical success of deep meta-learning, theoretical understanding of overparameterized meta-learning is still limited. This paper studies the generalization of a widely used meta-learning approach, Model-A... |
Title: Replacing Labeled Real-image Datasets with Auto-generated Contours Abstract: In the present work, we show that the performance of formula-driven supervised learning (FDSL) can match or even exceed that of ImageNet-21k without the use of real images, human-, and self-supervision during the pre-training of Vision ... |
Title: Tackling Spoofing-Aware Speaker Verification with Multi-Model Fusion Abstract: Recent years have witnessed the extraordinary development of automatic speaker verification (ASV). However, previous works show that state-of-the-art ASV models are seriously vulnerable to voice spoofing attacks, and the recently prop... |
Title: Deep Inverse Reinforcement Learning for Route Choice Modeling Abstract: Route choice modeling, i.e., the process of estimating the likely path that individuals follow during their journeys, is a fundamental task in transportation planning and demand forecasting. Classical methods generally adopt the discrete cho... |
Title: Nonparametric Multi-shape Modeling with Uncertainty Quantification Abstract: The modeling and uncertainty quantification of closed curves is an important problem in the field of shape analysis, and can have significant ramifications for subsequent statistical tasks. Many of these tasks involve collections of clo... |
Title: Pursuit of a Discriminative Representation for Multiple Subspaces via Sequential Games Abstract: We consider the problem of learning discriminative representations for data in a high-dimensional space with distribution supported on or around multiple low-dimensional linear subspaces. That is, we wish to compute ... |
Title: NISPA: Neuro-Inspired Stability-Plasticity Adaptation for Continual Learning in Sparse Networks Abstract: The goal of continual learning (CL) is to learn different tasks over time. The main desiderata associated with CL are to maintain performance on older tasks, leverage the latter to improve learning of future... |
Title: Bear the Query in Mind: Visual Grounding with Query-conditioned Convolution Abstract: Visual grounding is a task that aims to locate a target object according to a natural language expression. As a multi-modal task, feature interaction between textual and visual inputs is vital. However, previous solutions mainl... |
Title: Pre-training Enhanced Spatial-temporal Graph Neural Network for Multivariate Time Series Forecasting Abstract: Multivariate Time Series (MTS) forecasting plays a vital role in a wide range of applications. Recently, Spatial-Temporal Graph Neural Networks (STGNNs) have become increasingly popular MTS forecasting ... |
Title: Decoupled Dynamic Spatial-Temporal Graph Neural Network for Traffic Forecasting Abstract: We all depend on mobility, and vehicular transportation affects the daily lives of most of us. Thus, the ability to forecast the state of traffic in a road network is an important functionality and a challenging task. Traff... |
Title: Fast and Provable Tensor Robust Principal Component Analysis via Scaled Gradient Descent Abstract: An increasing number of data science and machine learning problems rely on computation with tensors, which better capture the multi-way relationships and interactions of data than matrices. When tapping into this c... |
Title: Tree-Guided Rare Feature Selection and Logic Aggregation with Electronic Health Records Data Abstract: Statistical learning with a large number of rare binary features is commonly encountered in analyzing electronic health records (EHR) data, especially in the modeling of disease onset with prior medical diagnos... |
Title: Score-Guided Intermediate Layer Optimization: Fast Langevin Mixing for Inverse Problems Abstract: We prove fast mixing and characterize the stationary distribution of the Langevin Algorithm for inverting random weighted DNN generators. This result extends the work of Hand and Voroninski from efficient inversion ... |
Title: The Consistency of Adversarial Training for Binary Classification Abstract: Robustness to adversarial perturbations is of paramount concern in modern machine learning. One of the state-of-the-art methods for training robust classifiers is adversarial training, which involves minimizing a supremum-based surrogate... |
Title: Existence and Minimax Theorems for Adversarial Surrogate Risks in Binary Classification Abstract: Adversarial training is one of the most popular methods for training methods robust to adversarial attacks, however, it is not well-understood from a theoretical perspective. We prove and existence, regularity, and ... |
Title: Fully Privacy-Preserving Federated Representation Learning via Secure Embedding Aggregation Abstract: We consider a federated representation learning framework, where with the assistance of a central server, a group of $N$ distributed clients train collaboratively over their private data, for the representations... |
Title: Fair Generalized Linear Models with a Convex Penalty Abstract: Despite recent advances in algorithmic fairness, methodologies for achieving fairness with generalized linear models (GLMs) have yet to be explored in general, despite GLMs being widely used in practice. In this paper we introduce two fairness criter... |
Title: Comment on Transferability and Input Transformation with Additive Noise Abstract: Adversarial attacks have verified the existence of the vulnerability of neural networks. By adding small perturbations to a benign example, adversarial attacks successfully generate adversarial examples that lead misclassification ... |
Title: Weakly Supervised Classification of Vital Sign Alerts as Real or Artifact Abstract: A significant proportion of clinical physiologic monitoring alarms are false. This often leads to alarm fatigue in clinical personnel, inevitably compromising patient safety. To combat this issue, researchers have attempted to bu... |
Title: Analysis & Computational Complexity Reduction of Monocular and Stereo Depth Estimation Techniques Abstract: Accurate depth estimation with lowest compute and energy cost is a crucial requirement for unmanned and battery operated autonomous systems. Robotic applications require real time depth estimation for navi... |
Title: Demystifying the Base and Novel Performances for Few-shot Class-incremental Learning Abstract: Few-shot class-incremental learning (FSCIL) has addressed challenging real-world scenarios where unseen novel classes continually arrive with few samples. In these scenarios, it is required to develop a model that reco... |
Title: CLiMB: A Continual Learning Benchmark for Vision-and-Language Tasks Abstract: Current state-of-the-art vision-and-language models are evaluated on tasks either individually or in a multi-task setting, overlooking the challenges of continually learning (CL) tasks as they arrive. Existing CL benchmarks have facili... |
Title: NASTAR: Noise Adaptive Speech Enhancement with Target-Conditional Resampling Abstract: For deep learning-based speech enhancement (SE) systems, the training-test acoustic mismatch can cause notable performance degradation. To address the mismatch issue, numerous noise adaptation strategies have been derived. In ... |
Title: Learning the parameters of a differential equation from its trajectory via the adjoint equation Abstract: The paper contributes to strengthening the relation between machine learning and the theory of differential equations. In this context, the inverse problem of fitting the parameters, and the initial conditio... |
Title: Beyond Rewards: a Hierarchical Perspective on Offline Multiagent Behavioral Analysis Abstract: Each year, expert-level performance is attained in increasingly-complex multiagent domains, notable examples including Go, Poker, and StarCraft II. This rapid progression is accompanied by a commensurate need to better... |
Title: Riemannian CUR Decompositions for Robust Principal Component Analysis Abstract: Robust Principal Component Analysis (PCA) has received massive attention in recent years. It aims to recover a low-rank matrix and a sparse matrix from their sum. This paper proposes a novel nonconvex Robust PCA algorithm, coined Rie... |
Title: Accelerating Machine Learning Training Time for Limit Order Book Prediction Abstract: Financial firms are interested in simulation to discover whether a given algorithm involving financial machine learning will operate profitably. While many versions of this type of algorithm have been published recently by rese... |
Title: Energy reconstruction for large liquid scintillator detectors with machine learning techniques: aggregated features approach Abstract: Large scale detectors consisting of a liquid scintillator (LS) target surrounded by an array of photo-multiplier tubes (PMT) are widely used in modern neutrino experiments: Borex... |
Title: Validation of Vector Data using Oblique Images Abstract: Oblique images are aerial photographs taken at oblique angles to the earth's surface. Projections of vector and other geospatial data in these images depend on camera parameters, positions of the geospatial entities, surface terrain, occlusions, and visibi... |
Title: AnyMorph: Learning Transferable Polices By Inferring Agent Morphology Abstract: The prototypical approach to reinforcement learning involves training policies tailored to a particular agent from scratch for every new morphology. Recent work aims to eliminate the re-training of policies by investigating whether a... |
Title: Stop Overcomplicating Selective Classification: Use Max-Logit Abstract: We tackle the problem of Selective Classification where the goal is to achieve the best performance on the desired coverages of the dataset. Recent state-of-the-art selective methods come with architectural changes either via introducing a s... |
Title: Binary Early-Exit Network for Adaptive Inference on Low-Resource Devices Abstract: Deep neural networks have significantly improved performance on a range of tasks with the increasing demand for computational resources, leaving deployment on low-resource devices (with limited memory and battery power) infeasible... |
Title: Landscape Learning for Neural Network Inversion Abstract: Many machine learning methods operate by inverting a neural network at inference time, which has become a popular technique for solving inverse problems in computer vision, robotics, and graphics. However, these methods often involve gradient descent thro... |
Title: Designing MacPherson Suspension Architectures using Bayesian Optimization Abstract: Engineering design is traditionally performed by hand: an expert makes design proposals based on past experience, and these proposals are then tested for compliance with certain target specifications. Testing for compliance is pe... |
Title: Conditional Permutation Invariant Flows Abstract: We present a novel, conditional generative probabilistic model of set-valued data with a tractable log density. This model is a continuous normalizing flow governed by permutation equivariant dynamics. These dynamics are driven by a learnable per-set-element term... |
Title: Path-Gradient Estimators for Continuous Normalizing Flows Abstract: Recent work has established a path-gradient estimator for simple variational Gaussian distributions and has argued that the path-gradient is particularly beneficial in the regime in which the variational distribution approaches the exact target ... |
Title: Diffusion models as plug-and-play priors Abstract: We consider the problem of inferring high-dimensional data $\mathbf{x}$ in a model that consists of a prior $p(\mathbf{x})$ and an auxiliary constraint $c(\mathbf{x},\mathbf{y})$. In this paper, the prior is an independently trained denoising diffusion generativ... |
Title: LIMO: Latent Inceptionism for Targeted Molecule Generation Abstract: Generation of drug-like molecules with high binding affinity to target proteins remains a difficult and resource-intensive task in drug discovery. Existing approaches primarily employ reinforcement learning, Markov sampling, or deep generative ... |
Title: Towards Efficient Active Learning of PDFA Abstract: We propose a new active learning algorithm for PDFA based on three main aspects: a congruence over states which takes into account next-symbol probability distributions, a quantization that copes with differences in distributions, and an efficient tree-based da... |
Title: Cluster Generation via Deep Energy-Based Model Abstract: We present a new approach for the generation of stable structures of nanoclusters using deep learning methods. Our method consists in constructing an artificial potential energy surface, with local minima corresponding to the most stable structures and whi... |
Title: A review of machine learning concepts and methods for addressing challenges in probabilistic hydrological post-processing and forecasting Abstract: Probabilistic forecasting is receiving growing attention nowadays in a variety of applied fields, including hydrology. Several machine learning concepts and methods ... |
Title: Robust Group Synchronization via Quadratic Programming Abstract: We propose a novel quadratic programming formulation for estimating the corruption levels in group synchronization, and use these estimates to solve this problem. Our objective function exploits the cycle consistency of the group and we thus refer ... |
Title: StaDRe and StaDRo: Reliability and Robustness Estimation of ML-based Forecasting using Statistical Distance Measures Abstract: Reliability estimation of Machine Learning (ML) models is becoming a crucial subject. This is particularly the case when such \mbox{models} are deployed in safety-critical applications, ... |
Title: Multi-scale Super-resolution Magnetic Resonance Spectroscopic Imaging with Adjustable Sharpness Abstract: Magnetic Resonance Spectroscopic Imaging (MRSI) is a valuable tool for studying metabolic activities in the human body, but the current applications are limited to low spatial resolutions. The existing deep ... |
Title: ck-means, a novel unsupervised learning method that combines fuzzy and crispy clustering methods to extract intersecting data Abstract: Clustering data is a popular feature in the field of unsupervised machine learning. Most algorithms aim to find the best method to extract consistent clusters of data, but very ... |
Title: Explainable Global Error Weighted on Feature Importance: The xGEWFI metric to evaluate the error of data imputation and data augmentation Abstract: Evaluating the performance of an algorithm is crucial. Evaluating the performance of data imputation and data augmentation can be similar since both generated data c... |
Title: DPDR: A novel machine learning method for the Decision Process for Dimensionality Reduction Abstract: This paper discusses the critical decision process of extracting or selecting the features in a supervised learning context. It is often confusing to find a suitable method to reduce dimensionality. There are pr... |
Title: Shallow and Deep Nonparametric Convolutions for Gaussian Processes Abstract: A key challenge in the practical application of Gaussian processes (GPs) is selecting a proper covariance function. The moving average, or process convolutions, construction of GPs allows some additional flexibility, but still requires ... |
Title: Random Forest of Epidemiological Models for Influenza Forecasting Abstract: Forecasting the hospitalizations caused by the Influenza virus is vital for public health planning so that hospitals can be better prepared for an influx of patients. Many forecasting methods have been used in real-time during the Influe... |
Title: Actionable Guidance for High-Consequence AI Risk Management: Towards Standards Addressing AI Catastrophic Risks Abstract: Artificial intelligence (AI) systems can provide many beneficial capabilities but also risks of adverse events. Some AI systems could present risks of events with very high or catastrophic co... |
Title: Transformer Neural Networks Attending to Both Sequence and Structure for Protein Prediction Tasks Abstract: The increasing number of protein sequences decoded from genomes is opening up new avenues of research on linking protein sequence to function with transformer neural networks. Recent research has shown tha... |
Title: Design of Multi-model Linear Inferential Sensors with SVM-based Switching Logic Abstract: We study the problem of data-based design of multi-model linear inferential (soft) sensors. The multi-model linear inferential sensors promise increased prediction accuracy yet simplicity of the model structure and training... |
Title: Intra-Instance VICReg: Bag of Self-Supervised Image Patch Embedding Abstract: Recently, self-supervised learning (SSL) has achieved tremendous empirical advancements in learning image representation. However, our understanding and knowledge of the representation are still limited. This work shows that the succes... |
Title: The Impact of Variable Ordering on Bayesian Network Structure Learning Abstract: Causal Bayesian Networks provide an important tool for reasoning under uncertainty with potential application to many complex causal systems. Structure learning algorithms that can tell us something about the causal structure of the... |
Title: Photoelectric Factor Prediction Using Automated Learning and Uncertainty Quantification Abstract: The photoelectric factor (PEF) is an important well logging tool to distinguish between different types of reservoir rocks because PEF measurement is sensitive to elements with high atomic number. Furthermore, the r... |
Title: Score-based Generative Models for Calorimeter Shower Simulation Abstract: Score-based generative models are a new class of generative algorithms that have been shown to produce realistic images even in high dimensional spaces, currently surpassing other state-of-the-art models for different benchmark categories ... |
Title: Learning a Single Neuron with Adversarial Label Noise via Gradient Descent Abstract: We study the fundamental problem of learning a single neuron, i.e., a function of the form $\mathbf{x}\mapsto\sigma(\mathbf{w}\cdot\mathbf{x})$ for monotone activations $\sigma:\mathbb{R}\mapsto\mathbb{R}$, with respect to the $... |
Title: The Open Catalyst 2022 (OC22) Dataset and Challenges for Oxide Electrocatalysis Abstract: Computational catalysis and machine learning communities have made considerable progress in developing machine learning models for catalyst discovery and design. Yet, a general machine learning potential that spans the chem... |
Title: SYMBA: Symbolic Computation of Squared Amplitudes in High Energy Physics with Machine Learning Abstract: The cross section is one of the most important physical quantities in high-energy physics and the most time consuming to compute. While machine learning has proven to be highly successful in numerical calcula... |
Title: Approximate Equivariance SO(3) Needlet Convolution Abstract: This paper develops a rotation-invariant needlet convolution for rotation group SO(3) to distill multiscale information of spherical signals. The spherical needlet transform is generalized from $\mathbb{S}^2$ onto the SO(3) group, which decomposes a sp... |
Title: Adapting the Linearised Laplace Model Evidence for Modern Deep Learning Abstract: The linearised Laplace method for estimating model uncertainty has received renewed attention in the Bayesian deep learning community. The method provides reliable error bars and admits a closed-form expression for the model eviden... |
Title: Popular decision tree algorithms are provably noise tolerant Abstract: Using the framework of boosting, we prove that all impurity-based decision tree learning algorithms, including the classic ID3, C4.5, and CART, are highly noise tolerant. Our guarantees hold under the strongest noise model of nasty noise, and... |
Title: Scaling multi-species occupancy models to large citizen science datasets Abstract: Citizen science datasets can be very large and promise to improve species distribution modelling, but detection is imperfect, risking bias when fitting models. In particular, observers may not detect species that are actually pres... |
Title: Representational Multiplicity Should Be Exposed, Not Eliminated Abstract: It is prevalent and well-observed, but poorly understood, that two machine learning models with similar performance during training can have very different real-world performance characteristics. This implies elusive differences in the int... |
Title: RetrievalGuard: Provably Robust 1-Nearest Neighbor Image Retrieval Abstract: Recent research works have shown that image retrieval models are vulnerable to adversarial attacks, where slightly modified test inputs could lead to problematic retrieval results. In this paper, we aim to design a provably robust image... |
Title: Lossy Compression with Gaussian Diffusion Abstract: We describe a novel lossy compression approach called DiffC which is based on unconditional diffusion generative models. Unlike modern compression schemes which rely on transform coding and quantization to restrict the transmitted information, DiffC relies on t... |
Title: Fast Population-Based Reinforcement Learning on a Single Machine Abstract: Training populations of agents has demonstrated great promise in Reinforcement Learning for stabilizing training, improving exploration and asymptotic performance, and generating a diverse set of solutions. However, population-based train... |
Title: Incorporating intratumoral heterogeneity into weakly-supervised deep learning models via variance pooling Abstract: Supervised learning tasks such as cancer survival prediction from gigapixel whole slide images (WSIs) are a critical challenge in computational pathology that requires modeling complex features of ... |
Title: CtrlFormer: Learning Transferable State Representation for Visual Control via Transformer Abstract: Transformer has achieved great successes in learning vision and language representation, which is general across various downstream tasks. In visual control, learning transferable state representation that can tra... |
Title: Improving Generalization of Metric Learning via Listwise Self-distillation Abstract: Most deep metric learning (DML) methods employ a strategy that forces all positive samples to be close in the embedding space while keeping them away from negative ones. However, such a strategy ignores the internal relationship... |
Title: Mirror Descent with Relative Smoothness in Measure Spaces, with application to Sinkhorn and EM Abstract: Many problems in machine learning can be formulated as optimizing a convex functional over a space of measures. This paper studies the convergence of the mirror descent algorithm in this infinite-dimensional ... |
Title: How robust are pre-trained models to distribution shift? Abstract: The vulnerability of machine learning models to spurious correlations has mostly been discussed in the context of supervised learning (SL). However, there is a lack of insight on how spurious correlations affect the performance of popular self-su... |
Title: Fast Lossless Neural Compression with Integer-Only Discrete Flows Abstract: By applying entropy codecs with learned data distributions, neural compressors have significantly outperformed traditional codecs in terms of compression ratio. However, the high inference latency of neural networks hinders the deploymen... |
Title: Plotly-Resampler: Effective Visual Analytics for Large Time Series Abstract: Visual analytics is arguably the most important step in getting acquainted with your data. This is especially the case for time series, as this data type is hard to describe and cannot be fully understood when using for example summary ... |
Title: Generalized Frank-Wolfe Algorithm for Bilevel Optimization Abstract: In this paper, we study a class of bilevel optimization problems, also known as simple bilevel optimization, where we minimize a smooth objective function over the optimal solution set of another convex constrained optimization problem. Several... |
Title: A Survey on Computational Intelligence-based Transfer Learning Abstract: The goal of transfer learning (TL) is providing a framework for exploiting acquired knowledge from source to target data. Transfer learning approaches compared to traditional machine learning approaches are capable of modeling better data p... |
Title: Avoid Overfitting User Specific Information in Federated Keyword Spotting Abstract: Keyword spotting (KWS) aims to discriminate a specific wake-up word from other signals precisely and efficiently for different users. Recent works utilize various deep networks to train KWS models with all users' speech data cent... |
Title: MineDojo: Building Open-Ended Embodied Agents with Internet-Scale Knowledge Abstract: Autonomous agents have made great strides in specialist domains like Atari games and Go. However, they typically learn tabula rasa in isolated environments with limited and manually conceived objectives, thus failing to general... |
Title: Channel-wise Mixed-precision Assignment for DNN Inference on Constrained Edge Nodes Abstract: Quantization is widely employed in both cloud and edge systems to reduce the memory occupation, latency, and energy consumption of deep neural networks. In particular, mixed-precision quantization, i.e., the use of diff... |
Title: SMPL: Simulated Industrial Manufacturing and Process Control Learning Environments Abstract: Traditional biological and pharmaceutical manufacturing plants are controlled by human workers or pre-defined thresholds. Modernized factories have advanced process control algorithms such as model predictive control (MP... |
Title: AutoML Two-Sample Test Abstract: Two-sample tests are important in statistics and machine learning, both as tools for scientific discovery as well as to detect distribution shifts. This led to the development of many sophisticated test procedures going beyond the standard supervised learning frameworks, whose us... |
Title: Random projections and Kernelised Leave One Cluster Out Cross-Validation: Universal baselines and evaluation tools for supervised machine learning for materials properties Abstract: With machine learning being a popular topic in current computational materials science literature, creating representations for com... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.