Papers by Nilesh Jain
EFTNAS: Searching for Efficient Language Models in First-Order Weight-Reordered Super-Networks (2024.lrec-main)
Copied to clipboard
| Challenge: | Depending on the size of transformer-based models, they can be restricted from deployment in resource-constrained environments. |
| Approach: | They propose to combine neural architecture search and network pruning techniques to generate and train weight-sharing super-networks that contain efficient transformer-based models. |
| Outcome: | The proposed model achieves high-performing, high-performance subnetworks on the general language understanding evaluation and the Stanford Question Answering Dataset. |
LoNAS: Elastic Low-Rank Adapters for Efficient Large Language Models (2024.lrec-main)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) reach hundreds of billions of parameters and require resources for training and inference stages. |
| Approach: | They propose a low-rank adapter to reduce the number of trainable parameters in a model and reduce memory requirements. |
| Outcome: | The proposed approach reduces memory and compute requirements while preserving performance. |
Mamba-Shedder: Post-Transformer Compression for Efficient Selective Structured State Space Models (2025.naacl-long)
Copied to clipboard
| Challenge: | Large pre-trained models have achieved outstanding results in sequence modeling . alternative architectures, such as Selective Structured State Space Models (SSMs), have been proposed to address these inefficiencies. |
| Approach: | They propose to reduce the size and computational overhead of large pre-trained models by removing selected components at different granularities. |
| Outcome: | The proposed models achieve a speedup of up to 1.4x during inference while maintaining accuracy. |
SQFT: Low-cost Model Adaptation in Low-precision Sparse Foundation Models (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Large pre-trained models are often adapted to a desired domain or task through a fine-tuning stage. |
| Approach: | They propose an end-to-end solution for sparse parameter-efficient fine-tuning of large pre-trained models. |
| Outcome: | The proposed approach can be used to combine sparse weights with low-rank adapters without losing sparsity and accuracy. |