6 papers
DeltaFlow: Noise-Adaptive Bidirectional Gated Delta Networks for Embedded Language Flows
Guangfu Guo, Xiaoqian Lu, Linsey Pang +4
Embedded Language Flows (ELF) rely primarily on full non-causal attention for iterative denoising, repeatedly incurring quadratic sequence-mixing cost at each sampling step. Gated…
HP-JEPA: Hierarchical Partitioning for Multi-Resolution Graph Joint-Embedding Predictive Learning
Ruichen Xu, Jingxiang Qu, Wenhan Gao +5
Graph self-supervised learning aims to learn transferable representations from large-scale unlabeled graph data. Joint-embedding predictive architectures (JEPAs) avoid explicit neg…
S-JEPA : Soft Clustering Anchors for Self-Supervised Speech Representation Learning
Georgios Ioannides, Adrian Kieback, Judah Goldfeder +5
Self-supervised speech encoders are predominantly trained by predicting discrete hard cluster IDs at masked positions, a recipe that collapses acoustic ambiguity at category bounda…
Beyond Soft Masks: Hard-Perturbation Mixup Explainer for Robust GNN Explainability
Jialiang Yin, Zheng Zhao, Linsey Pang +3
Graph Neural Networks (GNNs) have demonstrated remarkable performance across a range of applications involving graph-structured data, particularly in high-stakes domains. However,…
Soft Clustering Anchors for Self-Supervised Speech Representation Learning in Joint Embedding Prediction Architectures
Georgios Ioannides, Adrian Kieback, Judah Goldfeder +5
Joint Embedding Predictive Architectures (JEPA) offer a promising approach to self-supervised speech representation learning, but suffer from representation collapse without explic…
JEPA as a Neural Tokenizer: Learning Robust Speech Representations with Density Adaptive Attention
Georgios Ioannides, Christos Constantinou, Aman Chadha +4
We introduce a two-stage self-supervised framework that combines the Joint-Embedding Predictive Architecture (JEPA) with a Density Adaptive Attention Mechanism (DAAM) for learning…