:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Gutheil, Julian, Hitzginger, Simon, Legenstein, Robert
Format:	Preprint
Published:	2026
Subjects:	Machine Learning
Online Access:	https://arxiv.org/abs/2605.22472
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

IndiSeek learns information-guided disentangled representations
by: Gui, Yu, et al.
Published: (2025)

Restoring Vision in Adverse Weather Conditions with Patch-Based Denoising Diffusion Models
by: Özdenizci, Ozan, et al.
Published: (2022)

Union-over-Intersections: Object Detection beyond Winner-Takes-All
by: Bhowmik, Aritra, et al.
Published: (2023)

Adversarially Robust Spiking Neural Networks Through Conversion
by: Özdenizci, Ozan, et al.
Published: (2023)

Privacy-Aware Lifelong Learning
by: Özdenizci, Ozan, et al.
Published: (2025)

Winner-Take-All Column Row Sampling for Memory Efficient Adaptation of Language Model
by: Liu, Zirui, et al.
Published: (2023)

Breaking $\textit{Winner-Takes-All}$: Cooperative Policy Optimization Improves Diverse LLM Reasoning
by: Chen, Haoxuan, et al.
Published: (2026)

Sharp Capacity Thresholds in Linear Associative Memory: From Winner-Take-All to Listwise Retrieval
by: Barnfield, Nicholas, et al.
Published: (2026)

Superposition disentanglement of neural representations reveals hidden alignment
by: Longon, André, et al.
Published: (2025)

Sparsity regularization via tree-structured environments for disentangled representations
by: Layne, Elliot, et al.
Published: (2024)

Curriculum reinforcement learning with measurable task representation learning
by: Wen, Yongyan, et al.
Published: (2026)

Regularising NARX models with multi-task learning
by: Bee, Sarah, et al.
Published: (2025)

Adversarially Robust Spiking Neural Networks with Sparse Connectivity
by: Schmolli, Mathias, et al.
Published: (2025)

A Scalable Hybrid Training Approach for Recurrent Spiking Neural Networks
by: Baronig, Maximilian, et al.
Published: (2025)

A representation-learning game for classes of prediction tasks
by: Uzan, Neria, et al.
Published: (2024)

Variational decomposition autoencoding improves disentanglement of latent representations
by: Ziogas, Ioannis, et al.
Published: (2026)

Single- to multi-fidelity history-dependent learning with uncertainty quantification and disentanglement: application to data-driven constitutive modeling
by: Yi, Jiaxiang, et al.
Published: (2025)

Computational bottlenecks for denoising diffusions
by: Montanari, Andrea, et al.
Published: (2025)

Symmetry-enforcing neural networks with applications to constitutive modeling
by: Garanger, Kévin, et al.
Published: (2023)

Discrete, compositional, and symbolic representations through attractor dynamics
by: Nam, Andrew, et al.
Published: (2023)

Transferring disentangled representations: bridging the gap between synthetic and real images
by: Dapueto, Jacopo, et al.
Published: (2024)

Learning-to-learn enables rapid learning with phase-change memory-based in-memory computing
by: Ortner, Thomas, et al.
Published: (2024)

Joint auto-encoders: a flexible multi-task learning framework
by: Epstein, Baruch, et al.
Published: (2017)

Towards understanding neural collapse in supervised contrastive learning with the information bottleneck method
by: Wang, Siwei, et al.
Published: (2023)

Adaptive multi-gradient methods for quasiconvex vector optimization and applications to multi-task learning
by: Minh, Nguyen Anh, et al.
Published: (2024)

Inductive biases of multi-task learning and finetuning: multiple regimes of feature reuse
by: Lippl, Samuel, et al.
Published: (2023)

ESM-NBR: fast and accurate nucleic acid-binding residue prediction via protein language model feature representation and multi-task learning
by: Zeng, Wenwu, et al.
Published: (2023)

Barrier-enforced multi-objective optimization for direct point and sharp interval forecasting
by: Amnuaypongsa, Worachit, et al.
Published: (2026)

Machine-learning optimized measurements of chaotic dynamical systems via the information bottleneck
by: Murphy, Kieran A., et al.
Published: (2023)

The Winner-Take-All Dilemma
by: Kikuchi, Kazuya, et al.
Published: (2022)

Reuse out-of-year data to enhance land cover mapping via feature disentanglement and contrastive learning
by: Dantas, Cassio F., et al.
Published: (2024)

Partial information decomposition: redundancy as information bottleneck
by: Kolchinsky, Artemy
Published: (2024)

MTLComb: multi-task learning combining regression and classification tasks for joint feature selection
by: Cao, Han, et al.
Published: (2024)

Physical formula enhanced multi-task learning for pharmacokinetics prediction
by: Li, Ruifeng, et al.
Published: (2024)

Detailed delineation of the fetal brain in diffusion MRI via multi-task learning
by: Karimi, Davood, et al.
Published: (2024)

Gradient-based inference of abstract task representations for generalization in neural networks
by: Hummos, Ali, et al.
Published: (2024)

Task-conditioned adaptation of visual features in multi-task policy learning
by: Marza, Pierre, et al.
Published: (2024)

BELT-2: Bootstrapping EEG-to-Language representation alignment for multi-task brain decoding
by: Zhou, Jinzhao, et al.
Published: (2024)

Contribution of task-irrelevant stimuli to drift of neural representations
by: Pashakhanloo, Farhad
Published: (2025)

Active perception and disentangled representations allow continual, episodic zero and few-shot learning
by: Rawlinson, David, et al.
Published: (2026)