Title: UnDiMix: Hard Negative Sampling Strategies for Contrastive Representation Learning. Abstract: One of the challenges in contrastive learning is the selection of appropriate \textit{hard negative} examples, in the absence of label information. 
Random sampling or importance sampling methods based on feature similarity often lead to sub-optimal performance. 
In this work, we introduce \modelname, a hard negative sampling strategy that takes into account anchor similarity, model uncertainty and diversity. 
Experimental results on several benchmarks show that \modelname improves negative sample selection, and subsequently downstream performance when compared to state-of-the-art contrastive learning methods. Code is available at \textit{anon. link