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Found 9 papers, 1 papers with code
Relevant or Random: Can LLMs Truly Perform Analogical Reasoning?
One key finding in psychology is that compared with irrelevant past experiences, recalling relevant ones can help humans better handle new tasks.
Lifelong Event Detection with Embedding Space Separation and Compaction
However, the simple combination of memory data and new-task samples can still result in substantial forgetting of previously acquired knowledge, which may occur due to the potential overlap between the feature distribution of new data and the previously learned embedding space.
Data Augmentation using LLMs: Data Perspectives, Learning Paradigms and Challenges
In the rapidly evolving field of machine learning (ML), data augmentation (DA) has emerged as a pivotal technique for enhancing model performance by diversifying training examples without the need for additional data collection.
Chain of LoRA: Efficient Fine-tuning of Language Models via Residual Learning
Fine-tuning is the primary methodology for tailoring pre-trained large language models to specific tasks.
Improving In-context Learning via Bidirectional Alignment
Large language models (LLMs) have shown impressive few-shot generalization on many tasks via in-context learning (ICL).
Adaptive Gradient Methods with Local Guarantees
Adaptive gradient methods are the method of choice for optimization in machine learning and used to train the largest deep models.
Machine Learning for Mechanical Ventilation Control
We consider the problem of controlling an invasive mechanical ventilator for pressure-controlled ventilation: a controller must let air in and out of a sedated patient's lungs according to a trajectory of airway pressures specified by a clinician.
Fully Dynamic Inference with Deep Neural Networks
Modern deep neural networks are powerful and widely applicable models that extract task-relevant information through multi-level abstraction.
Efficient Synthesis of Compact Deep Neural Networks
These large, deep models are often unsuitable for real-world applications, due to their massive computational cost, high memory bandwidth, and long latency.
