The paper introduces Self-guided Masked Autoencoders (SMA), a method for self-supervised learning that works without domain-specific assumptions. It uses an attention-based model with a masked modeling objective to learn masks…
Continue readingThe article focuses on the impact of batch size on pre-training in self-supervised speech representation learning. The study uses varying batch sizes and observes that larger batch sizes result in…
Continue readingThe study presents aLLM4TS, an innovative framework that adapts Large Language Models (LLMs) for time-series representation learning. The approach reimagines time-series forecasting as a self-supervised, multi-patch prediction task, capturing temporal…
Continue readingThe article discusses the theoretical understanding of self-supervised learning (SSL) methods like SimCLR, CLIP, and VicREG. The authors propose a generative latent variable model to explain the principle behind these…
Continue readingThe research presents AMuSE (Adaptive Multimodal Analysis for Speaker Emotion), a model developed for recognizing individual emotions in group conversations. This model is crucial in developing intelligent agents for natural…
Continue readingThe paper introduces ESPnet-SPK, a toolkit for training speaker embedding extractors. It offers an open-source platform for the speaker recognition community to build models, ranging from x-vector to recent SKA-TDNN….
Continue readingThis research focuses on utilizing liver CT scans in colorectal carcinoma genomics mutation classification. The liver is the most affected organ by distant metastasis in colon-rectal cancer patients. The study…
Continue readingThe paper presents a new approach to crowdsourcing, leveraging self-supervised learning and a novel aggregation scheme. Instead of averaging estimates from crowdworkers, it adapts weights based on their previous estimates….
Continue readingThe article introduces a novel approach to Kriging, a method of estimating attributes of unseen locations based on neighboring data. The proposed method, called Contrastive- Prototypical self-supervised learning for Kriging…
Continue readingThe paper presents DeepRicci, a self-supervised model for graph structure-feature co-refinement. It aims to solve the over-squashing issue that is common in Graph Neural Networks (GNNs). This is achieved by…
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