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Ruslan Svirschevski
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2020 – today
- 2025
[c4]Roman Garipov, Fedor Velikonivtsev, Ivan Ermakov, Ruslan Svirschevski, Vage Egiazarian, Max Ryabinin:
AutoJudge: Judge Decoding Without Manual Annotation. NeurIPS 2025
[i6]Roman Garipov, Fedor Velikonivtsev, Ruslan Svirschevski, Vage Egiazarian, Max Ryabinin:
AutoJudge: Judge Decoding Without Manual Annotation. CoRR abs/2504.20039 (2025)- 2024
[c3]Tim Dettmers, Ruslan Svirschevski, Vage Egiazarian, Denis Kuznedelev, Elias Frantar, Saleh Ashkboos, Alexander Borzunov, Torsten Hoefler, Dan Alistarh:
SpQR: A Sparse-Quantized Representation for Near-Lossless LLM Weight Compression. ICLR 2024
[c2]Zhuoming Chen, Avner May, Ruslan Svirschevski, Yuhsun Huang, Max Ryabinin, Zhihao Jia, Beidi Chen:
Sequoia: Scalable and Robust Speculative Decoding. NeurIPS 2024
[c1]Ruslan Svirschevski, Avner May, Zhuoming Chen, Beidi Chen, Zhihao Jia, Max Ryabinin:
SpecExec: Massively Parallel Speculative Decoding For Interactive LLM Inference on Consumer Devices. NeurIPS 2024
[i5]Zhuoming Chen, Avner May, Ruslan Svirschevski, Yuhsun Huang, Max Ryabinin, Zhihao Jia, Beidi Chen:
Sequoia: Scalable, Robust, and Hardware-aware Speculative Decoding. CoRR abs/2402.12374 (2024)
[i4]Ruslan Svirschevski, Avner May, Zhuoming Chen, Beidi Chen, Zhihao Jia, Max Ryabinin:
SpecExec: Massively Parallel Speculative Decoding for Interactive LLM Inference on Consumer Devices. CoRR abs/2406.02532 (2024)
[i3]Vage Egiazarian, Denis Kuznedelev, Anton Voronov, Ruslan Svirschevski, Michael Goin, Daniil Pavlov, Dan Alistarh, Dmitry Baranchuk:
Accurate Compression of Text-to-Image Diffusion Models via Vector Quantization. CoRR abs/2409.00492 (2024)
[i2]Philip Zmushko, Marat Mansurov, Ruslan Svirschevski, Denis Kuznedelev, Max Ryabinin, Aleksandr Beznosikov:
Label Privacy in Split Learning for Large Models with Parameter-Efficient Training. CoRR abs/2412.16669 (2024)- 2023
[i1]Tim Dettmers, Ruslan Svirschevski, Vage Egiazarian, Denis Kuznedelev, Elias Frantar, Saleh Ashkboos, Alexander Borzunov, Torsten Hoefler, Dan Alistarh:
SpQR: A Sparse-Quantized Representation for Near-Lossless LLM Weight Compression. CoRR abs/2306.03078 (2023)
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