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09/05/2023
Efficiency is Not Enough: A Critical Perspective of Environmentally Sustainable AI
Artificial Intelligence (AI) is currently spearheaded by machine learnin...
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06/01/2023
Smooth Monotonic Networks
Monotonicity constraints are powerful regularizers in statistical modell...
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01/15/2023
BuildSeg: A General Framework for the Segmentation of Buildings
Building segmentation from aerial images and 3D laser scanning (LiDAR) i...
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12/18/2022
Mask-FPAN: Semi-Supervised Face Parsing in the Wild With De-Occlusion and UV GAN
Fine-grained semantic segmentation of a person's face and head, includin...
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12/18/2022
LR-CSNet: Low-Rank Deep Unfolding Network for Image Compressive Sensing
Deep unfolding networks (DUNs) have proven to be a viable approach to co...
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10/12/2022
Energy Consumption-Aware Tabular Benchmarks for Neural Architecture Search
The demand for large-scale computational resources for Neural Architectu...
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05/21/2022
Self-Supervised Speech Representation Learning: A Review
Although supervised deep learning has revolutionized speech and audio pr...
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03/30/2022
Remember to correct the bias when using deep learning for regression!
When training deep learning models for least-squares regression, we cann...
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03/01/2022
A Brief Overview of Unsupervised Neural Speech Representation Learning
Unsupervised representation learning for speech processing has matured g...
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12/21/2021
Deep Learning Based 3D Point Cloud Regression for Estimating Forest Biomass
Knowledge of forest biomass stocks and their development is important fo...
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11/29/2021
Do We Still Need Automatic Speech Recognition for Spoken Language Understanding?
Spoken language understanding (SLU) tasks are usually solved by first tr...
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06/25/2021
Chebyshev-Cantelli PAC-Bayes-Bennett Inequality for the Weighted Majority Vote
We present a new second-order oracle bound for the expected risk of a we...
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06/24/2021
Information Bottleneck: Exact Analysis of (Quantized) Neural Networks
The information bottleneck (IB) principle has been suggested as a way to...
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02/17/2021
Do End-to-End Speech Recognition Models Care About Context?
The two most common paradigms for end-to-end speech recognition are conn...
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02/01/2021
On Scaling Contrastive Representations for Low-Resource Speech Recognition
Recent advances in self-supervised learning through contrastive training...
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01/18/2021
Multimodal Variational Autoencoders for Semi-Supervised Learning: In Defense of Product-of-Experts
Multimodal generative models should be able to learn a meaningful latent...
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06/26/2020
A Loss Function for Generative Neural Networks Based on Watson's Perceptual Model
To train Variational Autoencoders (VAEs) to generate realistic imagery r...
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06/26/2020
On the convergence of the Metropolis algorithm with fixed-order updates for multivariate binary probability distributions
The Metropolis algorithm is arguably the most fundamental Markov chain M...
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04/29/2020
The International Workshop on Osteoarthritis Imaging Knee MRI Segmentation Challenge: A Multi-Institute Evaluation and Analysis Framework on a Standardized Dataset
Purpose: To organize a knee MRI segmentation challenge for characterizin...
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11/15/2019
Label-similarity Curriculum Learning
Curriculum learning can improve neural network training by guiding the o...
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11/05/2019
One Network to Segment Them All: A General, Lightweight System for Accurate 3D Medical Image Segmentation
Many recent medical segmentation systems rely on powerful deep learning ...
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10/24/2019
U-Time: A Fully Convolutional Network for Time Series Segmentation Applied to Sleep Staging
Neural networks are becoming more and more popular for the analysis of p...
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08/16/2019
Knowledge distillation for semi-supervised domain adaptation
In the absence of sufficient data variation (e.g., scanner and protocol ...
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10/23/2018
On PAC-Bayesian Bounds for Random Forests
Existing guarantees in terms of rigorous upper bounds on the generalizat...
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10/03/2018
PADDIT: Probabilistic Augmentation of Data using Diffeomorphic Image Transformation
For proper generalization performance of convolutional neural networks (...
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02/18/2018
Training Big Random Forests with Little Resources
Without access to large compute clusters, building random forests on lar...
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04/15/2017
Big Universe, Big Data: Machine Learning and Image Analysis for Astronomy
Astrophysics and cosmology are rich with data. The advent of wide-area d...
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08/19/2016
A Strongly Quasiconvex PAC-Bayesian Bound
We propose a new PAC-Bayesian bound and a way of constructing a hypothes...
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11/17/2015
Sacrificing information for the greater good: how to select photometric bands for optimal accuracy
Large-scale surveys make huge amounts of photometric data available. Bec...
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10/06/2015
Population-Contrastive-Divergence: Does Consistency help with RBM training?
Estimating the log-likelihood gradient with respect to the parameters of...
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03/31/2003
Recent Results on No-Free-Lunch Theorems for Optimization
The sharpened No-Free-Lunch-theorem (NFL-theorem) states that the perfor...
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04/16/2002
Neutrality: A Necessity for Self-Adaptation
Self-adaptation is used in all main paradigms of evolutionary computatio...
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08/21/2001