:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Jung, Wonjin, Kang, Sungil, Cho, Dong-Yeon
Format:	Preprint
Published:	2025
Subjects:	Machine Learning
Online Access:	https://arxiv.org/abs/2507.06802
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Multi-View Polymer Representations for the Open Polymer Prediction
by: Jung, Wonjin, et al.
Published: (2025)

State Estimation Using Particle Filtering in Adaptive Machine Learning Methods: Integrating Q-Learning and NEAT Algorithms with Noisy Radar Measurements
by: Song, Wonjin, et al.
Published: (2025)

FlowPath: Learning Data-Driven Manifolds with Invertible Flows for Robust Irregularly-sampled Time Series Classification
by: Oh, YongKyung, et al.
Published: (2025)

Stable Neural Stochastic Differential Equations in Analyzing Irregular Time Series Data
by: Oh, YongKyung, et al.
Published: (2024)

DualDynamics: Synergizing Implicit and Explicit Methods for Robust Irregular Time Series Analysis
by: Oh, YongKyung, et al.
Published: (2024)

Continuum Dropout for Neural Differential Equations
by: Lee, Jonghun, et al.
Published: (2025)

Modeling Irregular Astronomical Time Series with Neural Stochastic Delay Differential Equations
by: Oh, YongKyung, et al.
Published: (2025)

RepCodec: A Speech Representation Codec for Speech Tokenization
by: Huang, Zhichao, et al.
Published: (2023)

TANDEM: Temporal Attention-guided Neural Differential Equations for Missingness in Time Series Classification
by: Oh, YongKyung, et al.
Published: (2025)

Subspace-based Approximate Hessian Method for Zeroth-Order Optimization
by: Kim, Dongyoon, et al.
Published: (2025)

ConTEXTure: Consistent Multiview Images to Texture
by: Ahn, Jaehoon, et al.
Published: (2024)

Spectral Analysis of Representational Similarity with Limited Neurons
by: Kang, Hyunmo, et al.
Published: (2025)

Comprehensive Review of Neural Differential Equations for Time Series Analysis
by: Oh, YongKyung, et al.
Published: (2025)

RL-BioAug: Label-Efficient Reinforcement Learning for Self-Supervised EEG Representation Learning
by: Lee, Cheol-Hui, et al.
Published: (2026)

DiSTAR: Diffusion over a Scalable Token Autoregressive Representation for Speech Generation
by: Song, Yakun, et al.
Published: (2025)

Interdomain Attention: Beyond Token-Level Key-Value Memory
by: Kiyohara, Naoki, et al.
Published: (2026)

JEPA as a Neural Tokenizer: Learning Robust Speech Representations with Density Adaptive Attention
by: Ioannides, Georgios, et al.
Published: (2025)

Improving Self Consistency in LLMs through Probabilistic Tokenization
by: Sathe, Ashutosh, et al.
Published: (2024)

Measuring Intrinsic Dimension of Token Embeddings
by: Kataiwa, Takuya, et al.
Published: (2025)

Task-Induced Representational Invariances Depend on Learning Objective in Deep RL
by: Halvagal, Manu Srinath, et al.
Published: (2026)

AugWard: Augmentation-Aware Representation Learning for Accurate Graph Classification
by: Kim, Minjun, et al.
Published: (2025)

Multi-Task Corrupted Prediction for Learning Robust Audio-Visual Speech Representation
by: Kim, Sungnyun, et al.
Published: (2025)

DiffEditor: Enhancing Speech Editing with Semantic Enrichment and Acoustic Consistency
by: Chen, Yang, et al.
Published: (2024)

PhysioME: A Robust Multimodal Self-Supervised Framework for Physiological Signals with Missing Modalities
by: Lee, Cheol-Hui, et al.
Published: (2025)

Efficient Generative Modeling with Residual Vector Quantization-Based Tokens
by: Kim, Jaehyeon, et al.
Published: (2024)

Towards Improved Sentence Representations using Token Graphs
by: Mantri, Krishna Sri Ipsit, et al.
Published: (2026)

Laplacian Heads Improve Transformers by Smoothing Token Representations
by: Zhang, Yuchong, et al.
Published: (2026)

Predictive Causal Inference via Spatio-Temporal Modeling and Penalized Empirical Likelihood
by: Lee, Byunghee, et al.
Published: (2025)

Feasibility Consistent Representation Learning for Safe Reinforcement Learning
by: Cen, Zhepeng, et al.
Published: (2024)

Autoregressive Speech Enhancement via Acoustic Tokens
by: Della Libera, Luca, et al.
Published: (2025)

PAST: Phonetic-Acoustic Speech Tokenizer
by: Har-Tuv, Nadav, et al.
Published: (2025)

Scaling Speech Tokenizers with Diffusion Autoencoders
by: Wang, Yuancheng, et al.
Published: (2026)

Disentangled Representation Learning via Modular Compositional Bias
by: Jung, Whie, et al.
Published: (2025)

Efficient Training of Language Models with Compact and Consistent Next Token Distributions
by: Sathe, Ashutosh, et al.
Published: (2024)

WISTERIA: Learning Clinical Representations from Noisy Supervision via Multi-View Consistency in Electronic Health Records
by: Dong, Ruan, et al.
Published: (2026)

Estimating Dimensionality of Neural Representations from Finite Samples
by: Chun, Chanwoo, et al.
Published: (2025)

Token Sparse Attention: Efficient Long-Context Inference with Interleaved Token Selection
by: Jo, Dongwon, et al.
Published: (2026)

TaDiCodec: Text-aware Diffusion Speech Tokenizer for Speech Language Modeling
by: Wang, Yuancheng, et al.
Published: (2025)

Key Principles of Graph Machine Learning: Representation, Robustness, and Generalization
by: Abbahaddou, Yassine
Published: (2026)

TokenBlowUp: Resolving Representational Singularities in LLM Token Spaces via Monoidal Transformations
by: Zhao, Dongfang
Published: (2025)