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Found 8 papers, 1 papers with code
Exploring Quantization for Efficient Pre-Training of Transformer Language Models
While quantization has proven to be effective after pre-training and during fine-tuning, applying quantization in Transformers during pre-training has remained largely unexplored at scale for language modeling.
A Deep Dive into the Trade-Offs of Parameter-Efficient Preference Alignment Techniques
Large language models are first pre-trained on trillions of tokens and then instruction-tuned or aligned to specific preferences.
Language Models for Novelty Detection in System Call Traces
Due to the complexity of modern computer systems, novel and unexpected behaviors frequently occur.
A Practical Survey on Faster and Lighter Transformers
Recurrent neural networks are effective models to process sequences.
On Improving Deep Learning Trace Analysis with System Call Arguments
Such tasks may be used to detect anomalies, pre-train neural networks to improve their performance, and extract a contextual representation of the events.
DepGraph: Localizing Performance Bottlenecks in Multi-Core Applications Using Waiting Dependency Graphs and Software Tracing
In this paper, we use a system level tracing approach to extract a Waiting Dependency Graph that shows the breakdown of a task execution among all the interleaving threads and resources.
Software Engineering
Automatic Cause Detection of Performance Problems in Web Applications
In this paper, we propose a method of extracting the internal behavior of web requests as well as introduce a pipeline that detects performance issues in web requests and provides insights into their root causes.
Empirical comparison between autoencoders and traditional dimensionality reduction methods
The four different dimensionality reduction techniques were separately employed on each dataset to project data into a low-dimensional space.
