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This paper proposes minimizing the information content in neural network weights to enhance generalization, particularly when training data is scarce. It introduces a method where adaptable Gaussian noise is added to the weights, balancing the expected squared error against the amount of information the weights contain. Leveraging the Minimum Description Length (MDL) principle and a "bits back" argument for communicating these noisy weights, the approach enables efficient derivative computations, especially if output units are linear. The paper also explores using adaptive mixtures of Gaussians for more flexible prior distributions for weight coding. Preliminary results indicated a slight improvement over simple weight-decay on a high-dimensional task.

This post explores why physical systems’ “complexity” rises, peaks, then falls over time, unlike entropy, which always increases. Using Kolmogorov complexity and the notion of “sophistication,” the author proposes a formal way to capture this pattern, introducing the idea of “complextropy” — a complexity measure that’s low in both highly ordered and fully random states but peaks during intermediate, evolving phases. He suggests using computational resource bounds to make the measure meaningful and proposes both theoretical and empirical (e.g., using file compression) approaches to test this idea, acknowledging it as an open problem.

The 2012 paper “ImageNet Classification with Deep Convolutional Neural Networks” by Krizhevsky, Sutskever, and Hinton introduced AlexNet, a deep CNN that dramatically improved image classification accuracy on ImageNet, halving the top-5 error rate from \~26% to \~15%. Its innovations — like ReLU activations, dropout, GPU training, and data augmentation — sparked the deep learning revolution, laying the foundation for modern computer vision and advancing AI across industries.

This paper presents a method for applying dropout regularization to LSTMs by restricting it to non-recurrent connections, solving prior issues with overfitting in recurrent networks. It significantly improves generalization across diverse tasks including language modeling, speech recognition, machine translation, and image captioning. The technique allows larger RNNs to be effectively trained without compromising their ability to memorize long-term dependencies. This work helped establish dropout as a viable regularization strategy for RNNs and influenced widespread adoption in sequence modeling applications.

Stanford’s 10-week CS231n dives from first principles to state-of-the-art vision research, starting with image-classification basics, loss functions and optimization, then building from fully-connected nets to modern CNNs, residual and vision-transformer architectures. Lectures span training tricks, regularization, visualization, transfer learning, detection, segmentation, video, 3-D and generative models. Three hands-on PyTorch assignments guide students from k-NN/SVM through deep CNNs and network visualization, and a capstone project lets teams train large-scale models on a vision task of their choice, graduating with the skills to design, debug and deploy real-world deep-learning pipelines.

This tutorial explores the surprising capabilities of Recurrent Neural Networks (RNNs), particularly in generating coherent text character by character. It delves into how RNNs, especially when implemented with Long Short-Term Memory (LSTM) units, can learn complex patterns and structures in data, enabling them to produce outputs that mimic the style and syntax of the training material. The discussion includes the architecture of RNNs, their ability to handle sequences of varying lengths, and the challenges associated with training them, such as the vanishing gradient problem. Through various examples, the tutorial illustrates the potential of RNNs in tasks like language modeling and sequence prediction.

This tutorial explains how Long Short-Term Memory (LSTM) networks address the limitations of traditional Recurrent Neural Networks (RNNs), particularly their difficulty in learning long-term dependencies due to issues like vanishing gradients. LSTMs introduce a cell state that acts as a conveyor belt, allowing information to flow unchanged, and utilize gates (input, forget, and output) to regulate the addition, removal, and output of information. This architecture enables LSTMs to effectively capture and maintain long-term dependencies in sequential data

The paper “Deep Residual Learning for Image Recognition” (ResNet, 2015) introduced residual networks with shortcut connections, allowing very deep neural networks (over 100 layers) to be effectively trained by reformulating the learning task into residual functions (F(x) = H(x) − x). This innovation solved the degradation problem in deep models, achieving state-of-the-art results on ImageNet (winning ILSVRC 2015) and COCO challenges. Its impact reshaped the design of deep learning architectures across vision and non-vision tasks, becoming a foundational backbone in modern AI systems.

We introduce a new neural architecture to learn the conditional probability of an output sequence with elements that are discrete tokens corresponding to positions in an input sequence. Such problems cannot be trivially addressed by existent approaches such as sequence-to-sequence and Neural Turing Machines, because the number of target classes in each step of the output depends on the length of the input, which is variable. Problems such as sorting variable sized sequences, and various combinatorial optimization problems belong to this class. Our model solves the problem of variable size output dictionaries using a recently proposed mechanism of neural attention. It differs from the previous attention attempts in that, instead of using attention to blend hidden units of an encoder to a context vector at each decoder step, it uses attention as a pointer to select a member of the input sequence as the output. We call this architecture a Pointer Net (Ptr-Net). We show Ptr-Nets can be used to learn approximate solutions to three challenging geometric problems -- finding planar convex hulls, computing Delaunay triangulations, and the planar Travelling Salesman Problem -- using training examples alone. Ptr-Nets not only improve over sequence-to-sequence with input attention, but also allow us to generalize to variable size output dictionaries. We show that the learnt models generalize beyond the maximum lengths they were trained on. We hope our results on these tasks will encourage a broader exploration of neural learning for discrete problems.

The paper “Attention Is All You Need” (2017) introduced the Transformer — a novel neural architecture relying solely on self-attention, removing recurrence and convolutions. It revolutionized machine translation by dramatically improving training speed and translation quality (e.g., achieving 28.4 BLEU on English-German tasks), setting new state-of-the-art benchmarks. Its modular, parallelizable design opened the door to large-scale pretraining and fine-tuning, ultimately laying the foundation for modern large language models like BERT and GPT. This paper reshaped the landscape of NLP and deep learning, making attention-based models the dominant paradigm across many tasks.

reveals that language model performance improves predictably as you scale up model size, dataset size, and compute, following smooth power-law relationships. It shows that larger models are more sample-efficient, and optimally efficient training uses very large models on moderate data, stopping well before convergence. The work provided foundational insights that influenced the development of massive models like GPT-3 and beyond, shaping how the AI community understands trade-offs between size, data, and compute in building ever-stronger models.

This tutorial offers a detailed, line-by-line PyTorch implementation of the Transformer model introduced in "Attention Is All You Need." It elucidates the model's architecture—comprising encoder-decoder structures with multi-head self-attention and feed-forward layers—enhancing understanding through annotated code and explanations. This resource serves as both an educational tool and a practical guide for implementing and comprehending Transformer-based models.