Subham Thirani的动态

Despite remarkable breakthroughs in deep learning architectures, current models face significant limitations in processing extended sequences efficiently. Traditional recurrent neural networks (RNNs) compress information into fixed-size hidden states, while attention-based architectures like Transformers encounter quadratic complexity scaling, effectively limiting their context windows. The Titans architecture addresses these fundamental challenges through an innovative dual-memory framework that synergistically combines a modified short-term attention mechanism with a sophisticated neural long-term memory module. This novel architecture introduces several key innovations: a surprise-based memory update mechanism that selectively stores and retrieves historical context across sequences exceeding two million tokens; three distinct memory integration variants (Memory as Context, Memory as Gate, and Memory as Layer) that enable flexible information flow; and an efficient computational framework achieving O(m2d + kd) complexity while maintaining linear scaling in memory operations. Empirical evaluations demonstrate superior performance across diverse tasks including language modeling, time series forecasting, and needle-in-a-haystack retrieval scenarios. However, implementing this architecture at scale presents several technical challenges. Current hardware constraints, particularly in GPU and TPU architectures, create bottlenecks when handling the memory demands of million-token context windows. The optimization of training dynamics requires careful balancing between short-term attention and long-term memory updates, while the surprise-based memory mechanism necessitates sophisticated calibration across different domains and tasks. Additionally, the architecture currently lacks adaptive thresholding mechanisms for dynamic memory management, suggesting potential extensions through reinforcement learning-based prioritization and graph-structured memory representations. https://lnkd.in/ghYu3YNq

Neural layers in a convolutional neural network (CNN) can be split across multiple machines using a technique known as model parallelism. This approach divides the model itself (rather than the data) across different machines or devices. I have always been fascinated by the ways in which this splitting can be done. How to Split Neural Layers Across Machines 1. Sequential Layer Splitting: Each machine or device processes specific layers of the model. For example, Machine A handles layers 1-3, Machine B handles layers 4-6, and so on. The output of one machine is sent as input to the next machine. 2. Pipeline Parallelism: Similar to sequential splitting but optimized for overlapping computations. Machines process different mini-batches or layers simultaneously to maximize resource utilization. 3. Intra-Layer Parallelism: For layers that are computationally intensive, a single layer can be split across machines. This often involves dividing the computations within the layer, such as splitting feature maps or kernel computations. Frameworks and Libraries: Modern deep learning frameworks like PyTorch and TensorFlow support model parallelism. Libraries like DeepSpeed and PipeDream provide advanced tools for efficient pipeline parallelism. Challenges 1. Communication Overhead: Data transfer between machines can introduce latency, especially for large intermediate results. 2. Synchronization: Ensuring that machines work in harmony requires efficient synchronization. 3. Load Balancing: Uneven computational loads across machines can lead to inefficiency. Use Cases Model parallelism is particularly useful when: 1. The model is too large to fit in the memory of a single machine (e.g., large-scale transformers). 2. Some layers are computationally expensive and benefit from distributed computation. I will deal with some of these aspects in future articles in detail.

In the realm of computer vision, there are several fundamental algorithms and techniques that form the basis for various tasks and applications. Here are some of the key algorithms commonly used in computer vision: 1. **Image Filtering**: Techniques such as Gaussian blur, median filter, and bilateral filter are used for noise reduction and smoothing images, which helps in improving the quality of images for further processing. 2. **Edge Detection**: Algorithms like Canny edge detector, Sobel operator, and Prewitt operator are used to identify edges or boundaries in images, which are crucial for tasks like object detection and image segmentation. 3. **Feature Detection and Description**: Algorithms such as Scale-Invariant Feature Transform (SIFT), Speeded Up Robust Features (SURF), and Oriented FAST and Rotated BRIEF (ORB) are used to detect and describe keypoints or distinctive features in images, which are used for tasks like image matching, object recognition, and image stitching. 4. **Histogram Equalization**: A technique used to enhance the contrast of an image by redistributing pixel intensities, which helps in improving the visual appearance of images and making features more distinguishable. 5. **Corner Detection**: Algorithms like Harris corner detector and Shi-Tomasi corner detector are used to detect corners or interest points in images, which are important for tasks like image alignment, tracking, and calibration. 6. **Optical Flow**: Techniques such as Lucas-Kanade method and Horn-Schunck method are used to estimate the motion of objects between consecutive frames in a video sequence, which is crucial for tasks like object tracking and motion analysis. 7. **Segmentation**: Algorithms like k-means clustering, watershed algorithm, and graph-based segmentation are used to partition an image into meaningful regions or segments based on similarity criteria, which helps in tasks like object detection, image labeling, and image analysis. 8. **Convolutional Neural Networks (CNNs)**: Deep learning models like CNNs have revolutionized computer vision by automatically learning hierarchical features from raw pixel data, leading to state-of-the-art performance in tasks like image classification, object detection, and image generation. 9. **Generative Adversarial Networks (GANs)**: GANs are used to generate realistic images by training a generator network to produce images that are indistinguishable from real images, while a discriminator network tries to differentiate between real and fake images. GANs have applications in image synthesis, image editing, and image super-resolution. 10. **Transfer Learning**: Transfer learning is a technique where pre-trained models (usually CNNs) trained on large-scale datasets like ImageNet are fine-tuned on specific tasks or domains with limited labeled data. This approach is widely used to achieve good performance on new tasks with less data and computational resources.

Been studying Transformers as Part of my NLP curriculum at The University of Texas at Austin. This post is intended for my fellow cohort students who are studying NLP with me. Others, I warn just ignore my writing below if you dont have background on Deep Learning and NLP. So NLP folks, We are just touched the tip of the ice-berg where we learned the current state-of-art in the NLP world: Here is the timeline - Spring 2018 AI2 Company makes ELMo (Embeddings from Language Models) - Summer 2018 releases GPT (Generative Pre-trained Transformer based ELMo) - October 2018 BERT (Bidirectional Encoder Representations from Transformers) brings major changes over ELMo by brining Transformers over LSTMs (Long Short Term Memory Architecture - which is primarily a RNN designed for long range dependences in sequences, which used memory cells with gates (forget, input, output) to control information flow. **BERT Commercial applications** - **Google Cloud**: BERT in Natural Language API for sentiment, syntax, entity recognition. - **Microsoft Azure**: BERT in Cognitive Services for Text Analytics, Question Answering. - **Hugging Face**: Hosts, fine-tunes, deploys BERT for various NLP tasks. - **AWS Comprehend**: BERT for text analytics like keyphrase extraction, sentiment, entity recognition. - **OpenAI API**: GPT-based but related to BERT for NLP applications. - Widely used in customer support, recommendation engines, legal document processing. - Note : ChatGPT is not BERT architecture , but decoder only model. Now comes this paper from HuggingFace claiming LSTMs are taking over Transformers they train 200x faster and there performance is comparable to Transformers ?? ?? ??. Is this seismic shift in the NLP world ? Is this the state of art that's gone define next generation of chatbot ? who is behind this transformation ?? Need to add this paper to the reading list. Opinions , comments folks ?

Great post, Aymeric Roucher! The insights into the performance of simplified RNNs like minLSTM and minGRU are truly eye-opening. ?? Achieving results comparable to transformers while requiring fewer training steps is a significant advancement for efficiency in language modeling. This research challenges the assumption that only complex architectures lead to breakthroughs, reinforcing the idea that the quality of data and scaling can be more critical. Definitely worth diving into the paper for anyone interested in optimizing their models! ????

Revolutionizing AI with Mamba: A Survey of Its Capabilities and Future Directions Mamba’s architecture is a unique blend of concepts from recurrent neural networks (RNNs), Transformers, and state space models. This hybrid approach allows Mamba to harness the strengths of each architecture while mitigating their weaknesses. The innovative selection mechanism within Mamba is particularly noteworthy; it parameterizes the state space model based on the input, enabling the model to dynamically adjust its focus on relevant information. This adaptability is crucial for handling diverse data types and maintaining performance across various tasks. Mamba’s performance is a standout feature, demonstrating remarkable efficiency. It achieves up to three times faster computation on A100 GPUs compared to traditional Transformer models. This speedup is attributed to its ability to compute recurrently with a scanning method, which reduces the overhead associated with attention calculations. Moreover, Mamba’s near-linear scalability means that as the sequence length increases, the computational cost does not grow exponentially. This feature makes it feasible to process long sequences without incurring prohibitive resource demands, opening new avenues for deploying deep learning models in real-time applications..... Read our full take on this: https://lnkd.in/gEzQ28wp Paper: https://lnkd.in/gqKC3_fB

New preprint: ML-based identification of the interface regions for coupling local and nonlocal models Local-nonlocal coupling approaches combine the computational efficiency of local models and the accuracy of nonlocal models. However, the coupling process is challenging, requiring expertise to identify the interface between local and nonlocal regions. This study introduces a machine learning-based approach to automatically detect the regions in which the local and nonlocal models should be used in a coupling approach. This identification process uses the loading functions and provides as output the selected model at the grid points. Training is based on datasets of loading functions for which reference coupling configurations are computed using accurate coupled solutions, where accuracy is measured in terms of the relative error between the solution to the coupling approach and the solution to the nonlocal model. We study two approaches that differ from one another in terms of the data structure. The first approach, referred to as the full-domain input data approach, inputs the full load vector and outputs a full label vector. In this case, the classification process is carried out globally. The second approach consists of a window-based approach, where loads are preprocessed and partitioned into windows and the problem is formulated as a node-wise classification approach in which the central point of each window is treated individually. The classification problems are solved via deep learning algorithms based on convolutional neural networks. The performance of these approaches is studied on one-dimensional numerical examples using F1-scores and accuracy metrics. In particular, it is shown that the windowing approach provides promising results, achieving an accuracy of 0.96 and an F1-score of 0.97. These results underscore the potential of the approach to automate coupling processes, leading to more accurate and computationally efficient solutions for material science applications. https://lnkd.in/digiQbYS

Brilliant research. Simple and effective. When we hear about transformers and attention, we often get explanations of how the mechanism works, followed by what feels like a posteriori reasoning on why it’s so effective. But maybe the architecture itself isn’t as crucial as we think—as long as it’s fast and we can throw enough data at it, it works. So, we should stay skeptical when people offer intricate explanations for why the data meat grinder works.

Architectures are important, but so is data curation. A simplified version of 2014 models can easily outperform sota.

Very interesting paper. Rather than the transformer architecture, these simplified RNNs models might be more suitable for the real-world multivariate time-series data (clinical and financial) that have less size than traditional data (text, image, biological data).