14 citations · 33 across the 6 of their papers we have counts for
4 papers · 1 filter
Per-Axis Weight Deltas for Frequent Model Updates
Stefan Kuyumdzhiev, Radostin Cholakov
Serving many task-specialized LLM variants is often limited by the large size of fine-tuned checkpoints and the resulting cold-start latency. Since fine-tuned weights differ from t…
Fast Matrix Multiplications for Lookup Table-Quantized LLMs
Han Guo, William Brandon, Radostin Cholakov +3
The deployment of large language models (LLMs) is often constrained by memory bandwidth, where the primary bottleneck is the cost of transferring model parameters from the GPU's gl…
The GatedTabTransformer. An enhanced deep learning architecture for tabular modeling
Radostin Cholakov, Todor Kolev
There is an increasing interest in the application of deep learning architectures to tabular data. One of the state-of-the-art solutions is TabTransformer which incorporates an att…
Transformers predicting the future. Applying attention in next-frame and time series forecasting
Radostin Cholakov, Todor Kolev
Recurrent Neural Networks were, until recently, one of the best ways to capture the timely dependencies in sequences. However, with the introduction of the Transformer, it has been…