A type of artificial intelligence that focuses on designing systems that can learn from and make decisions based on data.
The article introduces Q-probing, a method that adapts a pre-trained language model to maximize a task-specific reward function. This approach sits between heavier methods like finetuning and lighter ones like…
Continue readingThis article discusses the use of big data analytics to classify earthwork-related locations (ERLs), which are significant sources of urban dust pollution. The researchers used dump truck trajectory, point of…
Continue readingThe article delves into the study of off-policy evaluation (OPE) in environments with complex observations, aiming to develop estimators that can avoid exponential dependence on the horizon. The authors explore…
Continue readingThe paper introduces a novel method for credit card fraud detection, the Causal Temporal Graph Neural Network (CaT-GNN). This method leverages causal invariant learning to reveal inherent correlations within transaction…
Continue readingThe article proposes a way to incorporate expert rules into machine learning models, specifically in the context of concept-based learning. The authors discuss methods to combine logical rules and neural…
Continue readingThe article presents Clifford-Steerable Convolutional Neural Networks (CS-CNNs), a new class of E(p, q)-equivariant CNNs. These networks process multivector fields on pseudo-Euclidean spaces Rp,q, offering significant improvements over baseline methods…
Continue readingThe study by Eshaan Nichani, Alex Damian, and Jason D. Lee from Princeton University investigates how transformers learn causal structures using gradient descent. The research reveals that transformers’ success in…
Continue readingThis article discusses the application of Reinforcement Learning from Human Feedback (RLHF) in large language models (LLMs). It critically examines the use of Proximal Policy Optimization (PPO), which though popular,…
Continue readingThe paper delves into the theoretical understanding of fine-tuning methods such as prompting and prefix-tuning of transformer models. It posits that these methods can universally approximate sequence-to-sequence functions, and that…
Continue readingThe paper delves into the concept of realisability in statistical learning theory under the assumption of epistemic uncertainty. The author considers a scenario where the train and test distribution are…
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