:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Demirel, Ilker, Shi, Lawrence, Hussain, Zeshan, Sontag, David
Format:	Preprint
Published:	2026
Subjects:	Artificial Intelligence
Online Access:	https://arxiv.org/abs/2603.11679
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Uncovering Bias Mechanisms in Observational Studies
by: Demirel, Ilker, et al.
Published: (2025)

Benchmarking Observational Studies with Experimental Data under Right-Censoring
by: Demirel, Ilker, et al.
Published: (2024)

Prediction-powered Generalization of Causal Inferences
by: Demirel, Ilker, et al.
Published: (2024)

Towards efficient representation identification in supervised learning
by: Ahuja, Kartik, et al.
Published: (2022)

Chain-of-thought obfuscation learned from output supervision can generalise to unseen tasks
by: Hadida, Nathaniel Mitrani, et al.
Published: (2026)

A self-supervised framework for learning whole slide representations
by: Hou, Xinhai, et al.
Published: (2024)

Large language models can learn and generalize steganographic chain-of-thought under process supervision
by: Skaf, Joey, et al.
Published: (2025)

From task structures to world models: What do LLMs know?
by: Yildirim, Ilker, et al.
Published: (2023)

Leveraging large language models for efficient representation learning for entity resolution
by: Xu, Xiaowei, et al.
Published: (2024)

LLMs can Schedule
by: Abgaryan, Henrik, et al.
Published: (2024)

Self-supervised inter-intra period-aware ECG representation learning for detecting atrial fibrillation
by: Zhu, Xiangqian, et al.
Published: (2024)

No Need for Explanations: LLMs can implicitly learn from mistakes in-context
by: Alazraki, Lisa, et al.
Published: (2025)

An overview of neural architectures for self-supervised audio representation learning from masked spectrograms
by: Yadav, Sarthak, et al.
Published: (2025)

Universal scaling laws in quantum-probabilistic machine learning by tensor network towards interpreting representation and generalization powers
by: Bai, Sheng-Chen, et al.
Published: (2024)

Perturbation: A simple and efficient adversarial tracer for representation learning in language models
by: Rozner, Joshua, et al.
Published: (2026)

Learning Without Augmenting: Unsupervised Time Series Representation Learning via Frame Projections
by: Demirel, Berken Utku, et al.
Published: (2025)

Humans can learn to detect AI-generated texts, or at least learn when they can't
by: Milička, Jiří, et al.
Published: (2025)

Self-supervised vision-langage alignment of deep learning representations for bone X-rays analysis
by: Englebert, Alexandre, et al.
Published: (2024)

The impact of hyper automation on streamlining enterprise digital workflows
by: Ishaan Rathore
Published: (2022)

Keep what you need : extracting efficient subnetworks from large audio representation models
by: Genova, David, et al.
Published: (2025)

Resource-efficient Layer-wise Federated Self-supervised Learning
by: Tun, Ye Lin, et al.
Published: (2024)

Abnormalities and Disease Detection in Gastro-Intestinal Tract Images
by: Khan, Zeshan, et al.
Published: (2026)

LLMs Gaming Verifiers: RLVR can Lead to Reward Hacking
by: Helff, Lukas, et al.
Published: (2026)

Large language models streamline automated systematic review: A preliminary study
by: Chen, Xi, et al.
Published: (2025)

Large Language Models, scientific knowledge and factuality: A framework to streamline human expert evaluation
by: Wysocka, Magdalena, et al.
Published: (2023)

Quota-based debiasing can decrease representation of already underrepresented groups
by: Smirnov, Ivan, et al.
Published: (2020)

Self-supervised learning on gene expression data
by: Dradjat, Kevin, et al.
Published: (2025)

Semantic representations emerge in biologically inspired ensembles of cross-supervising neural networks
by: Urbach, Roy, et al.
Published: (2025)

Boosting multi-demographic federated learning for chest radiograph analysis using general-purpose self-supervised representations
by: Lotfinia, Mahshad, et al.
Published: (2025)

LLMs can be easily Confused by Instructional Distractions
by: Hwang, Yerin, et al.
Published: (2025)

LLMs can Realize Combinatorial Creativity: Generating Creative Ideas via LLMs for Scientific Research
by: Gu, Tianyang, et al.
Published: (2024)

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

Harnessing the power of LLMs for normative reasoning in MASs
by: Savarimuthu, Bastin Tony Roy, et al.
Published: (2024)

Improved cystic hygroma detection from prenatal imaging using ultrasound-specific self-supervised representation learning
by: Megahed, Youssef, et al.
Published: (2025)

How well can LLMs provide planning feedback in grounded environments?
by: Li, Yuxuan, et al.
Published: (2025)

How well can LLMs Grade Essays in Arabic?
by: Ghazawi, Rayed, et al.
Published: (2025)

SS-DPPN: A self-supervised dual-path foundation model for the generalizable cardiac audio representation
by: Muna, Ummy Maria, et al.
Published: (2025)

LLMs can Compress LLMs: Adaptive Pruning by Agents
by: Kodathala, Sai Varun, et al.
Published: (2026)

Generating Energy-efficient code with LLMs
by: Cappendijk, Tom, et al.
Published: (2024)

Online inductive learning from answer sets for efficient reinforcement learning exploration
by: Veronese, Celeste, et al.
Published: (2025)