PiQA: an algebra for querying protein data sets
By Sandeep Tata, Jignesh M. Patel. 2003
55 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Molecular Biology, Computational Biology & Bioinformatics
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Elias Frantar
- Dan Alistarh
- Torsten Hoefler
- Saleh Ashkboos
- Volodymyr Kuleshov
- Chris De Sa
- Jerry Chee
- Yaohui Cai
In Molecular Biology
- Volodymyr Kuleshov
- Elias Frantar
- Torsten Hoefler
- Chris De Sa
- Dan Alistarh
- Jerry Chee
- Yaohui Cai
- Saleh Ashkboos
In Computational Biology & Bioinformatics
- Volodymyr Kuleshov
- Elias Frantar
- Torsten Hoefler
- Chris De Sa
- Dan Alistarh
- Jerry Chee
- Yaohui Cai
- Saleh Ashkboos
Citations in the corpus
- GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers (2022, methodology): “While our focus is on language generation, we also evaluate the performance of quantized models on some popular zero-shot tasks, namely LAMBADA (Paperno et al., 2016), ARC (Easy and Challenge) (Boratko et al., 2018) and PIQA (Tata & Patel, 2003).”
- SparseGPT: Massive Language Models Can Be Accurately Pruned in One-Shot (2023, methodology): “Yet, for additional interpretability, we also provide some ZeroShot accuracy results for LAMBADA [35], ARC (Easy and Challenge) [2], PIQA [44] and StoryCloze [33].”
- QuIP: 2-Bit Quantization of Large Language Models With Guarantees (2023, methodology): “We also evaluate on zero-shot tasks, including LAMBADA (LAMB) [21], ARC Easy (ArcE) [1], PiQA [26], and StoryCloze (SC) [18].”
- SpQR: A Sparse-Quantized Representation for Near-Lossless LLM Weight Compression (2023, unknown): “Secondly, we measure zero-shot accuracy on five tasks: WinoGrande [SBBC21], PiQA [TP03], HellaSwag, ARC-easy and ARC-challenge [CCE18].”