Search Results for author:
Found 9 papers, 4 papers with code
Verifix: Post-Training Correction to Improve Label Noise Robustness with Verified Samples
We introduce Verifix, a novel Singular Value Decomposition (SVD) based algorithm that leverages a small, verified dataset to correct the model weights using a single update.
Deep Unlearning: Fast and Efficient Training-free Approach to Controlled Forgetting
We demonstrate our algorithm's efficacy on ImageNet using a Vision Transformer with only $\sim$1. 5% drop in retain accuracy compared to the original model while maintaining under 1% accuracy on the unlearned class samples.
Neighborhood Gradient Clustering: An Efficient Decentralized Learning Method for Non-IID Data Distributions
Our experiments demonstrate that \textit{NGC} and \textit{CompNGC} outperform (by $0-6\%$) the existing SoTA decentralized learning algorithm over non-IID data with significantly less compute and memory requirements.
Low Precision Decentralized Distributed Training over IID and non-IID Data
In this paper, we propose and show the convergence of low precision decentralized training that aims to reduce the computational complexity and communication cost of decentralized training.
BERMo: What can BERT learn from ELMo?
Experiments on the probing task from SentEval dataset show that our model performs up to $4. 65\%$ better in accuracy than the baseline with an average improvement of $2. 67\%$ on the semantic tasks.
Intra-class Mixup for Out-of-Distribution Detection
This reduces the separability of in-distribution data from OoD data.
Out-of-Distribution Detection
Out of Distribution (OOD) Detection
Exploring Vicinal Risk Minimization for Lightweight Out-of-Distribution Detection
We utilize mixup in two ways to implement Vicinal Risk Minimization.
Out-of-Distribution Detection
Out of Distribution (OOD) Detection
TREND: Transferability based Robust ENsemble Design
In this work, we study the effect of network architecture, initialization, optimizer, input, weight and activation quantization on transferability of adversarial samples.
IMAC: In-memory multi-bit Multiplication andACcumulation in 6T SRAM Array
`In-memory computing' is being widely explored as a novel computing paradigm to mitigate the well known memory bottleneck.
