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Found 17 papers, 10 papers with code
Gradient-Free Adaptive Global Pruning for Pre-trained Language Models
The transformative impact of large language models (LLMs) like LLaMA and GPT on natural language processing is countered by their prohibitive computational demands.
Uncertainty Quantification for In-Context Learning of Large Language Models
Existing works have been devoted to quantifying the uncertainty in LLM's response, but they often overlook the complex nature of LLMs and the uniqueness of in-context learning.
Beyond Efficiency: A Systematic Survey of Resource-Efficient Large Language Models
We categorize methods based on their optimization focus: computational, memory, energy, financial, and network resources and their applicability across various stages of an LLM's lifecycle, including architecture design, pretraining, finetuning, and system design.
POND: Multi-Source Time Series Domain Adaptation with Information-Aware Prompt Tuning
In order to tackle these challenges simultaneously, in this paper, we introduce PrOmpt-based domaiN Discrimination (POND), the first framework to utilize prompts for time series domain adaptation.
Visual Attention Prompted Prediction and Learning
However, in many real-world situations, it is usually desired to prompt the model with visual attention without model retraining.
XAI Benchmark for Visual Explanation
Our proposed benchmarks facilitate a fair evaluation and comparison of visual explanation methods.
Saliency-Guided Hidden Associative Replay for Continual Learning
Continual Learning is a burgeoning domain in next-generation AI, focusing on training neural networks over a sequence of tasks akin to human learning.
Staleness-Alleviated Distributed GNN Training via Online Dynamic-Embedding Prediction
It utilizes an offline memory to cache historical information (e. g., node embedding) as an affordable approximation of the exact value and achieves high concurrency.
Domain Generalization Deep Graph Transformation
As a result, domain generalization graph transformation that predicts graphs not available in the training data is under-explored, with multiple key challenges to be addressed including (1) the extreme space complexity when training on all input-output mode combinations, (2) difference of graph topologies between the input and the output modes, and (3) how to generalize the model to (unseen) target domains that are not in the training data.
Knowledge-enhanced Neural Machine Reasoning: A Review
Knowledge-enhanced neural machine reasoning has garnered significant attention as a cutting-edge yet challenging research area with numerous practical applications.
Saliency-Augmented Memory Completion for Continual Learning
Specifically, we innovatively propose to store the part of the image most important to the tasks in episodic memory by saliency map extraction and memory encoding.
Deep Spatial Domain Generalization
Spatial domain generalization is a spatial extension of domain generalization, which can generalize to unseen spatial domains in continuous 2D space.
Saliency-Regularized Deep Multi-Task Learning
Specifically, we propose to model the task relation as the similarity between task input gradients, with a theoretical analysis of their equivalency.
RES: A Robust Framework for Guiding Visual Explanation
Despite the fast progress of explanation techniques in modern Deep Neural Networks (DNNs) where the main focus is handling "how to generate the explanations", advanced research questions that examine the quality of the explanation itself (e. g., "whether the explanations are accurate") and improve the explanation quality (e. g., "how to adjust the model to generate more accurate explanations when explanations are inaccurate") are still relatively under-explored.
Distributed Graph Neural Network Training with Periodic Stale Representation Synchronization
Traditional sampling-based methods accelerate GNN training by dropping edges and nodes, which impairs the graph integrity and model performance.
Temporal Domain Generalization with Drift-Aware Dynamic Neural Networks
Temporal domain generalization is a promising yet extremely challenging area where the goal is to learn models under temporally changing data distributions and generalize to unseen data distributions following the trends of the change.
Sign-regularized Multi-task Learning
Multi-task learning is a framework that enforces different learning tasks to share their knowledge to improve their generalization performance.
