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Tuesday, September 17, 2024

Multi-column Deep Neural Networks for Image Classification
In (Cireşan, Meier, and Schmidhuber 2012) titled “Multi-column Deep Neural Networks for Image Classification”, the authors, Dan Cireşan, Ueli Meier, Juergen Schmidhuber…
Tuesday, September 17, 2024
 
Improving Neural Networks by Preventing Co-Adaptation of Feature Detectors
In (Hinton et al. 2012) titled “Improving Neural Networks by Preventing Co-Adaptation of Feature Detectors”, the authors, Hinton, Geoffrey E., Nitish Srivastava, Alex…
Tuesday, September 17, 2024
 
ImageNet Classification with Deep Convolutional Neural Networks
(Krizhevsky, Sutskever, and Hinton 2012) is a seminal paper in the field of deep learning. It introduced the AlexNet architecture, which won the ImageNet Large Scale Visual…
Tuesday, September 17, 2024

NIN — Network in Network
In (Lin, Chen, and Yan 2014) the authors, Lin, Min, Qiang Chen, and Shuicheng Yan, of this paper titled “Network in Network” paper came up with a way of connencting somee…
Tuesday, September 17, 2024

Dropout: A Simple Way to Prevent Neural Networks from Overfitting
In (Srivastava et al. 2014) the authors, present a novel regularization technique for deep neural networks called “dropout.” The key idea behind dropout is to randomly drop…
Tuesday, September 17, 2024

ViT — An Image is worth 16x16 words: Transformers for Image Recognition at scale
While the Transformer architecture has become the de-facto standard for natural language processing tasks, its applications to computer vision remain limited. In vision…
Tuesday, September 17, 2024

LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders
In (BehnamGhader et al. 2024) the authors consider using LLMs which are mostly decoder only transformers as text encoders. This allows them to use the LLMs for NLP tasks…
Tuesday, September 17, 2024

MambaVision A Hybrid Mamba-Transformer Vision Backbone
In (Hatamizadeh and Kautz 2024), the authors apply the State Space Model (SSM) inherent in recently introduced Mamba architecture, (Gu and Dao 2023), for vision tasks. They…
Tuesday, September 17, 2024

Q*: Improving Multi-step Reasoning for LLMs with Deliberative Planning
The announcement of the GPT-5 strawberry model has sparked a lot of interest in this paper which seems to be the theory behind Open.ai’s new model.
Tuesday, September 17, 2024

Smaller, Weaker, Yet Better: Training LLM Reasoners via Compute-Optimal Sampling
in (Bansal et al. 2024) the authors consider the trade-offs between generating synthetic data using a stronger but more expensive (SE) model versus a weaker but cheaper (WC)…
Tuesday, September 17, 2024

TheoremLlama An End-To-End Framework to Train a General-Purpose Large Language Model to Become a Lean4 Expert
Proving mathematical theorems using computer-verifiable formal languages like Lean significantly impacts mathematical reasoning. One approach to formal theorem proving…
Tuesday, September 17, 2024
 
Tree Attention: Topology-Aware Decoding for Long-Context Attention on GPU Clusters
in (Shyam et al. 2024) the authors propose a new algorithm for parallelizing attention computation across multiple GPUs. This enables cross-device decoding to be performed…
Tuesday, September 17, 2024
 
2BP: 2-Stage Backpropagation
in (Shyam et al. 2024) the authors …
Tuesday, September 17, 2024

Why bother reviewing papers?
Why bother reviewing papers?
Tuesday, September 17, 2024
 
Dependency Parsing using Deep Learning
Remark 2. Explina the two approches to parsing
Tuesday, September 17, 2024
 
Why Overfitting Isn’t Always Bad: Retrofitting Cross-Lingual Word Embeddings to Dictionaries
This paper, “Why Overfitting Isn’t Always Bad: Retrofitting Cross-Lingual Word Embeddings to Dictionaries,” challenges the traditional view that overfitting is inherently…
Tuesday, June 11, 2024
 
sense2vec - A Fast and Accurate Method for Word Sense Disambiguation In Neural Word Embeddings
Sense2Vec (Trask, Michalak, and Liu 2015) is an interesting deep learning model based on word2vec that can learn more interesting and detailed word vectors from large…
Sunday, June 26, 2022

Skeletonization: A Technique for Trimming the Fat from a Network via Relevance Assessment
This pre GPU era Nips 1988 paper that talks about simplifying neural networks by removing redundant units. The authors present a novel approach to simplifying neural…
Wednesday, June 22, 2022
 
Simplifying Neural Networks by soft weight sharing
The primary aim of the paper (Nowlan and Hinton 1992) is reducing the complexity of neural networks by employing a mixture of Gaussian priors to the weights, creating a…
Wednesday, June 22, 2022

VGGNet: Very Deep Convolutional Networks for Large-Scale Image Recognition
In this paper (Simonyan and Zisserman 2015) the authors, Karen Simonyan and Andrew Zisserman from the Visual Geometry Group at Oxford, investigated the effect of increasing…
Thursday, December 10, 2015
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