Compression is from 98,304 to 576 bits (171x compression). Compression is from 98,304 to 144 bits (683x compression). The far left and right images are originals. B.1 Motivation In this section we outline the probabilistic model that motivates the HQA loss: L = log p (x | z = k) H [ q ( z |x)] + E A desired property of the HQA, motivated in Section 4.4, is the non-deterministic posterior We contrast these two models in Figure 8. This model is a V ariational Autoencoder with a simple Mixture of Gaussians prior.

Section 2: Experimental study of a different policy with fixed N and flexible M. Section 3: Sensitivity of hyper-parameter β In the main paper, we assume a policy with fixed M and flexible N. Furthermore, we also use a design space with N equal to a power-of-two. This is achieved by transforming the schemes of fixed M. For instance, 8:16, 4:16, 2:16 and 1:16 will be transformed as 1:2, 1:4, 1:8 and 1:16 with fixed N (1) and flexible M (2,4,8,16). Results are shown in Table 3. Figure 1 and 2 illustrate the differences between 1:2 and 2:4 with the same dense weight matrix and sparsity (i.e. Details can be found in Section 3.4 of the main paper. It consists of more than 1.2 million training images and Each image is labelled as one of 1K classes.

This study presents a comparative analysis of deep learning methodologies such as BERT, FinBERT and ULMFiT for sentiment analysis of earnings call transcripts. The objective is to investigate how Natural Language Processing (NLP) can be leveraged to extract sentiment from large-scale financial transcripts, thereby aiding in more informed investment decisions and risk management strategies. We examine the strengths and limitations of each model in the context of financial sentiment analysis, focusing on data preprocessing requirements, computational efficiency, and model optimization. Through rigorous experimentation, we evaluate their performance using key metrics, including accuracy, precision, recall, and F1-score. Furthermore, we discuss potential enhancements to improve the effectiveness of these models in financial text analysis, providing insights into their applicability for real-world financial decision-making.

Weaknesses: There are a number of small weakness to the approach, the technique to some degree depends on well registered images and there are a number of extra hyper parameters introduced, such as the number of partitions to use per 3D volume, the number of pre-trained decoder blocks to use and the region size. I would expect these aspects to be largely problem dependent, and the degree to which results would also be improved on other problems is therefore somewhat unclear. I do not think this invalidates the above comment. Aside from the approach itself, I would also have liked some information on training time and convergence. How easy is this to setup, train and add to existing training processes?

There's some space to improve for the experiments. I think the main contribution of this paper is proposing a method to transform the complicated neural network structure to a nonlinear feature mapping function, so that they can linearly separate the weight and feature mapping. Given the feature mapping, kernels/correlations and posterior distributions over output functions can be explicitly built for BNN (or DNN). Therefore, I would expect to see 1. What does this feature mapping look like? I think the authors show the kernel instead of the mapping itself.

Originality: This work builds on recent work on adapting deep networks for use with instrumental variables (DeepIV [Hartford et al 2017] & Adversarial GMM (AGMM) [Lewis & Syrgkanis 2018]) but adapts the optimally weighted GMM [Hansen 1982] (OWGMM) for the task. AGMM is probably most similar in that it is also an adversarial loss, but the variational reformulation presented in this paper results in a far simpler algorithm. Quality: I thought this was great paper. The variational reformulation of OWGMM leads to a far simpler objective function that neatly leverages the explosion of recent work in adversarial learning (GANs, etc.) by replacing a large number of moment conditions with a single adversarial network. That said, given that the method appears useful in practice, I would have liked to see more detailed experiments on the practical considerations.