We propose a fast approximation method of a softmax function with a very large vocabulary using singular value decomposition (SVD).

Conventional models prepare a large embedding matrix whose size depends on the vocabulary size. Therefore, storing these models in memory and disk storage is costly.

We show that it provides significant benefits to RL agents on a suite of gridworld, CARL and MetaWorld tasks.

HR-based orthogonal fine-tuning is equivalent to an adaptive low-rank adaptation.

Homomorphic encryption (HE)-based deep neural network (DNN) inference protects data and model privacy but suffers from significant computation overhead. We observe transforming the DNN weights into circulant matrices converts general matrix-vector multiplications into HE-friendly 1-dimensional convolutions, drastically reducing the HE computation cost.

The following proof is from Daniely and V ardi [15], and we give it here for completeness. By Lemma A.1, there exists a DNF formula We construct such an affine layer in Lemma A.2. At least one of the k size-n slices in z contains 0 more than once. We define the outputs of our affine layer as follows. Pr [z represents a hyperedge ] = n (n 1) ... (n k + 1) null 1 n null Pr null z Z null 1 2 log(n) .

Gaussian, and the weight matrix is non-degenerate.

Code is available at github.com/spcl/QuaRot .