We thank the reviewers for their detailed and thoughtful comments. These are not new and have been presented thoroughly in the submitted paper. Our intention was not to challenge the momentum mechanism. Combining SwA V with a momentum encoder and/or a large memory bank are indeed interesting follow-ups. In Tab.5, we make a best effort fair comparison (same data augmentation, num.

We take advantage of NF preconditioning and NF based Metropolis-Hastings updates for a faster convergence.

Although BERT -style encoder models are heavily used in NLP research, many researchers do not pretrain their own BERTs from scratch due to the high cost of training. In the past half-decade since BERT first rose to prominence, many advances have been made with other transformer architectures and training configurations that have yet to be systematically incorporated into BERT.

The box represents the first and third quartiles, and the whiskers represent the 10 and 90 percentiles. Each data point has a top-k score of objective value.

Although BERT -style encoder models are heavily used in NLP research, many researchers do not pretrain their own BERTs from scratch due to the high cost of training. In the past half-decade since BERT first rose to prominence, many advances have been made with other transformer architectures and training configurations that have yet to be systematically incorporated into BERT.

First provide a summary of the paper, and then address the following criteria: Quality, clarity, originality and significance. This paper generalizes the LSH method to account for the (bounded) lengths of the data base vectors, so that the LSH tricks for fast approximate nearest neighbor search can exploit the well-known relation between Euclidian distance and dot product similarity (e.g. as in equation 2) and support MIPS search as well. They give 3 motivating examples where solving MIPS vs kNN per se is more appropriate and needed. Their algorithm is essentially equation 9 (using equation 7 compute vector reformulations Q(q) and P(x) of the query a database element respectively). This is based on apparently novel observation (equation 8) that the distance from the query converges to the dot product plus a constant, when a parameter m which exponentiated the P(x) vector elements is sufficiently large (e.g.

We take advantage of NF preconditioning and NF based Metropolis-Hastings updates for a faster convergence.