ISCA Archive Interspeech 2020 ISCA Archive Interspeech 2020 Effect of Adding Positional Information on Convolutional Neural Networks for End-to-End Speech Recognition Jinhwan Park, Wonyong Sung Attention-based models with convolutional encoders enable faster training and inference than recurrent neural network-based ones. However, convolutional models often require a very large receptive field to achieve high recognition accuracy, which not only increases the parameter size but also the computational cost a
jarxiv Japanese arxiv コンテンツへスキップ ホーム ← Differentially Private Non-convex Learning for Multi-layer Neural Networks Data-Centric Learning from Unlabeled Graphs with Diffusion Model → Understanding Sparse Feature Updates in Deep Networks using Iterative Linearisation 投稿日: 2023年10月13日 作成者: jarxiv 要約 大規模で深いネットワークは、オーバーフィットする能力が増加しているにもかかわらず、うまく一般化します
How can we build agents that keep learning from experience, quickly and efficiently, after their initial training? Here we take inspiration from the main mechanism of learning in biological brains: synaptic plasticity, c…
NeurIPS 2019 Sun Dec 8th through Sat the 14th, 2019 at Vancouver Convention Center Paper ID: 3292 Title: Intrinsic dimension of data representations in deep neural networks This paper received mixed reviews from reviewers. Two reviewers are fairly strongly in favor, and one reviewer is fairly strongly against. The rebuttal clarified some points, but there was still some concern about lack of theoretical insights. While theory is important for advancing our field, empirical results often precede theory
# Neural Variational Inference: Importance Weighted Autoencoders ## Neural Variational Inference: Importance Weighted Autoencoders July 14, 2016 Previously we covered Variational Autoencoders (VAE) — popular inference tool based on neural networks. In this post we'll consider, a followup work from Torronto by Y. Burda, R. Grosse and R. Salakhutdinov, Importance Weighted Autoencoders (IWAE). The crucial contribution of this work is introduction of a new lower-bound on the marginal log-likelihood $\log p(x
## Most of the examples are in the following format: This is as simple a neural network I can think of. 2 inputs and xOR decision and on output. An xOr decision means: off output if neither input is on or both inputs are on. On output if either of the inputs are on but the other one has to be off 0, 0 = 0 1, 0 = 1 0, 1 = 1 1, 1 = 0 Click LearnXOR() then select which sets of inputs you want to test. this is fairly complex but many Neural Networks will have a similar structure ## Tensorflowjs xOr Neura
A long time ago in the machine learning literature, the idea of incorporating a mechanism inspired by the human visual system into neural networks was introduced. This idea is named the attention mechanism, and it has gone through a long development period. Today, many works have been devoted to this idea in a variety of tasks. Remarkable performance has recently been demonstrated. The goal of this paper is to provide an overview from the early work on searching for ways to implement attention idea with neu
Browser-based neural network playground. Choose datasets, configure hidden layers, pick activations, and watch decision boundaries form in real time. All training runs locally in your browser
A mechanism-first reading of how deterministic register automata can turn black-box sequence models into interpretable, robustness-checkable surrogates.
# Resource Evaluation for Neural Network Implementation on Xilinx Zynq Series FPGAs 2026-08-05 Follow us for more exciting content! Evaluation of Neural Network Implementation on Xilinx Zynq Series FPGAs Table of Contents 1. Memory Usage 1.1 Memory Implementation in FPGA Programs 1.2 BRAM Memory Size of Zynq 1.3 Memory Usage for a Convolution Operation 2. Feasibility of PipeCNN Analysis of PipeCNN Paper: Accelerating Large Convolution Networks on FPGA Using OpenCL 2.1 Resource Consumption of Impl