1 citations · 1 across the 4 of their papers we have counts for
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Do You Even Need More Data? Rethinking LLM Deployment for Label-Scarce Tabular Classification with TabLens
Daria Grushina, Kseniia Kuvshinova, Alina Kostromina +3
Every tabular classification task requires sufficient labeled data. When labels are scarce, practitioners can collect more examples, encode domain expertise into features, or accep…
Tsururu: A Python-based Time Series Forecasting Strategies Library
Alina Kostromina, Kseniia Kuvshinova, Aleksandr Yugay +2
While current time series research focuses on developing new models, crucial questions of selecting an optimal approach for training such models are underexplored. Tsururu, a Pytho…
Motivating Next-Gen Accelerators with Flexible (N:M) Activation Sparsity via Benchmarking Lightweight Post-Training Sparsification Approaches
Shirin Alanova, Kristina Kazistova, Ekaterina Galaeva +7
The demand for efficient large language model (LLM) inference has intensified the focus on sparsification techniques. While semi-structured (N:M) pruning is well-established for we…
Towards Foundation Time Series Model: To Synthesize Or Not To Synthesize?
Kseniia Kuvshinova, Olga Tsymboi, Alina Kostromina +2
The industry is rich in cases when we are required to make forecasting for large amounts of time series at once. However, we might be in a situation where we can not afford to trai…