BERT-based Emotion Recognition in Dialog Systems

Model Overview BERT (Bidirectional Encoder Representations from Transformers) is a language model developed by Google that uses Transformer encoder architecture with bidirectional context processing. Unlike traditional recurrent networks, BERT processes input sequences in both directions simultaneously, enabling comprehensive contextual underst ...

Posted on Thu, 03 Sep 2026 16:11:08 +0000 by mattheww

Hybrid Attention Transformer for Image Restoration

Hybrid Attention Transformer (HAT) Paper HAT: Hybrid Attention Transformer for Image Restoration Architecture Overview The HAT model consists of three main components: shallow feature extraction, deep feature extraction, and image reconstruction. Algorithm Principle The HAT approach integrates channel attention and window-based self-attention m ...

Posted on Sat, 15 Aug 2026 16:06:04 +0000 by hairytea

Transformer Model Architecture and Computational Analysis

Model Structure The basic unit consists of token embedding with positional encoding, encoder, and decoder. Encoder: Self-attention layer with skip connections and layer normalization, followed by a feed-forward network (FFN) with skip connections and layer normalization. Decoder: Self-attention layer with skip connections and layer normalizati ...

Posted on Wed, 24 Jun 2026 17:35:17 +0000 by Sul

Efficient Attention Mechanisms and Memory Optimization in Deep Learning

Attention Mechanisms Multi-Head Attention The attention mechanism computes: The scaling factor \(\sqrt{d_k}\) prevents large inner product values that could cause gradient instability. Assuming Q and K elements have mean 0 and variance \(\sigma^2\), the variance of \(QK^T\) grows with \(d_k\). Scaling by \(\sqrt{d_k}\) maintains stable varianc ...

Posted on Thu, 18 Jun 2026 17:39:50 +0000 by bschaeffer

Attention Mechanisms and Transformers: A Comprehensive Technical Overview

Attention Mechanisms and Transformers The attention mechanism addresses a fundamental challenge in deep learning: transforming variable-dimensional inputs into fixed-dimensional outputs through a weighted aggregation process. This capability proves essential when dealing with sequences or sets of varying sizes, where traditional fixed-parameter ...

Posted on Tue, 26 May 2026 17:04:19 +0000 by MilesStandish

MobileFormer: Efficient Hybrid Architecture for Local-Global Feature Fusion

MobileFormer introduces a novel architecture that synergistically combines the strengths of convolutional neural networks (CNNs) and Transformers to achieve high efficiency with minimal computational overhead. By leveraging a lightweight bidirectional bridge between a mobile backbone and a compact Transformer, it enables effective exchange of l ...

Posted on Wed, 20 May 2026 20:19:31 +0000 by n00854180t