We present a novel enhanced cyclic coordinate descent (ECCD) framework for solving generalized linear models with elastic net constraints that reduces training time in comparison to existing state-of-the-art methods. We redesign the CD method by performing a Taylor expansion around the current iterate to avoid nonlinear operations arising in the gradient computation. By introducing this approximation we are able to unroll the vector recurrences occurring in the CD method and reformulate the resulting computations into more efficient batched computations. We show empirically that the recurrence can be unrolled by a tunable integer parameter, s, such that s > 1 yields performance improvements without affecting convergence, whereas s= 1 yields the original CD method. A key advantage of ECCD is that it avoids the convergence delay and numerical instability exhibited by block coordinate descent. Finally, we implement our proposed method in C++ using Eigen to accelerate linear algebra computations. Comparison of our method against existing state-of-the-art solvers show consistent performance improvements of 3 in average for regularization path variant on diverse benchmark datasets. Our implementation is available at https://github.

A.1 Mach-Zehnder Interferometers (MZIs) A basic coherent optical component used in this work is an MZI. One of the most general MZI structures is shown in Figure 15, consisting of two 50-by-50 optical directional couplers and four phase shifters θ, θ, ω, and ω. An MZI can achieve arbitrary 2 2 unitary matrices SU(2). Figure 15: 2-by-2 MZI with top (T), left (L), upper (P), and lower (W) phase shifters. A.2 MZI-based Photonic Tensor Core Architecture By cascading N(N 1)/2MZIs into a triangular mesh (Recks-style) or rectangular mesh (Clementsstyle), we can construct arbitrary N N unitary U(N).

Masked diffusion language models (MDLMs) have recently emerged as a new paradigm in language modeling, offering flexible generation dynamics and enabling efficient parallel decoding. However, existing decoding strategies for pre-trained MDLMs predominantly rely on token-level uncertainty criteria, while largely overlooking sequence-level information and inter-token dependencies. To address this limitation, we propose Dependency-Oriented Sampler (DOS), a training-free decoding strategy that leverages inter-token dependencies to inform token updates during generation. Specifically, DOS exploits attention matrices from transformer blocks to approximate inter-token dependencies, emphasizing information from unmasked tokens when updating masked positions. Empirical results demonstrate that DOS consistently achieves superior performance on both code generation and mathematical reasoning tasks. Moreover, DOS can be seamlessly integrated with existing parallel sampling methods, leading to improved generation efficiency without sacrificing generation quality.

This paper introduces a novel prior called Diversified Block Sparse Prior to characterize the widespread block sparsity phenomenon in real-world data.

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.

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To overcome this limitation, we propose a novel blockwise parallel decoding scheme in which we makepredictions for multiple time steps inparallel then back offtothe longest prefix validated byascoring model.