Speech Data Selection for Efficient ASR Fine-Tuning using Domain Classifier and Pseudo-Label Filtering

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The rapid evolution of Machine Learning (ML) workloads, particularly Deep Neural Networks (DNNs) and Transformer-based models, has intensified demands on computing architectures, highlighting the limitations of traditional von Neumann systems due to the me ...

We investigate group fairness regularizers in federated learning, aiming to train a globally fair model in a distributed setting. Ensuring global fairness in distributed training presents unique challenges, as fairness regularizers typically involve probab ...

Metal ions are indispensable cofactors for many proteins and play crucial roles in numerous biological processes, yet the computational design of metalloproteins and the optimization of metal coordination sites poses significant challenges due to the comp ...

While classical in many theoretical settings-and in particular in statistical physics-inspired works-the assumption of Gaussian i.i.d. input data is often perceived as a strong limitation in the context of statistics and machine learning. In this study, we ...

We investigate the training dynamics of two-layer neural networks when learning multi-index target functions. We focus on multi-pass gradient descent (GD) that reuses the batches multiple times and show that it significantly changes the conclusion about wh ...

Multi-object spectroscopic galaxy surveys typically make use of photometric and colour criteria to select their targets. That is not the case of Euclid, which will use the NISP slitless spectrograph to record spectra for every source over its field of view ...

The Vision Transformer (ViT) excels in accuracy when handling high-resolution images, yet it confronts the challenge of significant spatial redundancy, leading to increased computational and memory requirements. To address this, we present the Localization ...

The success of machine learning (ML) has been intimately linked with the availability of large amounts of data, typically collected from heterogeneous sources and processed on vast networks of computing devices (also called workers). Beyond accuracy, the u ...

This dataset contains images (TIFF image data) of brain juvenile rats Wistar Han (P14) scanned immunostained slides by using an automated slide scanner (Olympus, VS120-L100, GER) equipped with a UPLSAPO 20x/0.75 air objective (Olympus, GER) and a Pike F5 ...

Reinforcement learning (RL) with dense rewards and imitation learning (IL) with human-generated trajectories are the most widely used approaches for training modern embodied agents. RL requires extensive reward shaping and auxiliary losses and is often too ...

Studying the intricacies of individual subjects' moods and cognitive processing over extended periods of time presents a formidable challenge in medicine. While much of systems neuroscience appropriately focuses on the link between neural circuit functions ...

Intelligent Fault Diagnosis (IFD) based on deep learning has proven to be an effective and flexible solution, attracting extensive research. Deep neural networks can learn rich representations from vast amounts of representative labeled data for various ap ...

Saliency models are image-based prediction models that estimate human visual attention. Such models, when applied to architectural spaces, could pave the way for design decisions where visual attention is taken into account. In this study, we tested the pe ...

This study investigates the use of a low cost microphone combined with state-of-the-art machine learning (ML) algorithms as online process monitoring to differentiate various materials and process regimes of Laser-Powder Bed Fusion (LPBF). Three processing ...

Loop closure detection helps simultaneous localization and mapping systems reduce map and state uncertainty via recognizing previously visited places along the path of a mobile robot. However, visual loop closure detection is susceptible to scenes with dyn ...

Offshore wind farm modelling has been an area of rapidly increasing interest over the last two decades, with numerous analytical as well as computational-based approaches developed, in an attempt to produce designs that improve wind farm efficiency in powe ...

With the onset of large-scale astronomical surveys capturing millions of images, there is an increasing need to develop fast and accurate deconvolution algorithms that generalize well to different images. A powerful and accessible deconvolution method woul ...

Large deep neural network (DNN) models pose the key challenge to energy efficiency due to the significantly higher energy consumption of off-chip DRAM accesses than arithmetic or SRAM operations. It motivates the intensive research on model compression wit ...

Edge devices must support computationally demanding algorithms, such as neural networks, within tight area/energy budgets. While approximate computing may alleviate these constraints, limiting induced errors remains an open challenge. In this paper, we pro ...

Streams are key components of the global carbon (C) cycle. The river network conceptualization moved from the pipelineâ idea, in which C is solely transported, to the active ecosystemâ concept, in which both terrestrial derived and autochthonous C ...

Speech Data Selection for Efficient ASR Fine-Tuning using Domain Classifier and Pseudo-Label Filtering

Chattez avec Graph Search

Stop Wasting your Cache! Bringing Machine Learning into Cache Computing

Global Group Fairness in Federated Learning via Function Tracking

Engineering of stable and functional metalloproteins using evolutionary computation, deep learning and simulation

Gaussian universality of perceptrons with random labels

The Benefits of Reusing Batches for Gradient Descent in Two-Layer Networks: Breaking the Curse of Information and Leap Exponents

Euclid: Testing photometric selection of emission-line galaxy targets?

LF-ViT: Reducing Spatial Redundancy in Vision Transformer for Efficient Image Recognition

Brief Announcement: A Case for Byzantine Machine Learning

Nissl_5, Raw images for Machine learning for histological annotation and quantification of cortical layers

SPOC: Imitating Shortest Paths in Simulation Enables Effective Navigation and Manipulation in the Real World

Ethological computational psychiatry: Challenges and opportunities

Calibrated Adaptive Teacher for Domain Adaptive Intelligent Fault Diagnosis

Saliency prediction in 360° architectural scenes: Performance and impact of daylight variations

Differentiation of materials and laser powder bed fusion processing regimes from airborne acoustic emission combined with machine learning

PlaceNet: A multi-scale semantic-aware model for visual loop closure detection

Offshore wind farm wake modelling using deep feed forward neural networks for active yaw control and layout optimisation

Deep learning-based galaxy image deconvolution

Non-Structured DNN Weight Pruning--Is It Beneficial in Any Platform?

Exact Neural Networks from Inexact Multipliers via Fibonacci Weight Encoding

Modeling of Stream Metabolism from Reach-scale to the Scale of Entire River Networks