Marktechpost

OCTOBER 21, 2024

Existing methods to address the challenges in AI-powered chess and decision-making systems include neural networks for chess, diffusion models, and world models. In chess AI, the field has evolved from handcrafted search algorithms and heuristics to neural network-based approaches.

Neural Network 102

Neural Network Inference Engine Large Language Models AI 102

Marktechpost

OCTOBER 20, 2024

They also present the EquiformerV2 model, a state-of-the-art Graph Neural Network (GNN) trained on the OMat24 dataset, achieving leading results on the Matbench Discovery leaderboard. The dataset includes diverse atomic configurations sampled from both equilibrium and non-equilibrium structures.

Neural Network 111

Neural Network Inference Engine AI AI 111

Marktechpost

OCTOBER 23, 2024

Shallow neural networks are used to map these relationships, so they fail to capture their depth. Traditional embedding methods, such as 2D Matryoshka Sentence Embeddings (2DMSE), have been used to represent data in vector space, but they struggle to encode the depth of complex structures. Don’t Forget to join our 55k+ ML SubReddit.

NLP 111

NLP Neural Network Natural Language Processing Inference Engine 111

Marktechpost

OCTOBER 21, 2024

The proposed methodology is rooted in the concept of Walk-Jump Sampling, where noise is added to clean data, followed by training a neural network to denoise it, thereby allowing a smooth sampling process. If you like our work, you will love our newsletter. Don’t Forget to join our 50k+ ML SubReddit.

Neural Network 103

Neural Network Inference Engine Machine Learning ML 103

Marktechpost

OCTOBER 17, 2024

Static Embeddings are bags of token embeddings that are summed together to create text embeddings, allowing for lightning-fast embeddings without requiring neural networks. [link] Introduction of Static Embeddings Another major feature is Static Embeddings, a modernized version of traditional word embeddings like GLoVe and word2vec.

Neural Network 103

Neural Network Inference Engine NLP ML 103

Marktechpost

OCTOBER 19, 2024

Inaccurate predictions in these cases can have real-world consequences, such as in engineering designs or scientific simulations where precision is critical. HNNs are particularly effective for systems where energy conservation holds but struggle with systems that violate this principle. If you like our work, you will love our newsletter.

Deep Learning 103

Deep Learning Neural Network Robotics Inference Engine 103

Unite.AI

JUNE 21, 2024

One such library is cuDNN (CUDA Deep Neural Network library), which provides highly tuned implementations of standard routines used in deep neural networks. The model is first parsed and optimized by TensorRT, which generates a highly optimized inference engine tailored for the specific model and hardware.

Deep Learning 130

Deep Learning Neural Network Convolutional Neural Networks Large Language Models 130

Marktechpost

OCTOBER 22, 2024

When a model receives an input, it processes it through multiple layers of neural networks, where each layer adjusts the model’s understanding of the task. Activation steering operates by identifying and manipulating the internal layers of the model responsible for instruction-following.

Large Language Models 111

Large Language Models Neural Network Inference Engine AI 111

Marktechpost

OCTOBER 14, 2024

raising widespread concerns about privacy threats of Deep Neural Networks (DNNs). Upcoming Live Webinar- Oct 29, 2024] The Best Platform for Serving Fine-Tuned Models: Predibase Inference Engine (Promoted) The post MIBench: A Comprehensive AI Benchmark for Model Inversion Attack and Defense appeared first on MarkTechPost.

Categorization 103

Categorization Neural Network Inference Engine Deep Learning 103

Mlearning.ai

MAY 16, 2023

Deployment of deep neural network on mobile phone. (a) Introduction As more and more deep neural networks, like CNNs, Transformers, and Large Language Models (LLMs), generative models, etc., to boost the usages of the deep neural networks in our lives. 1], (d) image by Shiwa ID on Unsplash. 2] Android.

Neural Network 98

Neural Network Convolutional Neural Networks Deep Learning Inference Engine 98

Marktechpost

OCTOBER 21, 2024

A significant aspect of AI research focuses on neural networks, particularly transformers. Several tools have been developed to study how neural networks operate. During training, neural networks adjust their weights based on how well they minimize prediction errors (loss).

Machine Learning 60

Machine Learning Neural Network Natural Language Processing Inference Engine 60

Marktechpost

OCTOBER 16, 2024

More sophisticated machine learning approaches, such as artificial neural networks (ANNs), may detect complex relationships in data. Furthermore, deep learning techniques like convolutional networks (CNNs) and long short-term memory (LSTM) models are commonly employed due to their ability to analyze temporal and meteorological data.

Machine Learning 64

Machine Learning Neural Network Deep Learning Inference Engine 64

Marktechpost

OCTOBER 15, 2024

XAI, or Explainable AI, brings about a paradigm shift in neural networks that emphasizes the need to explain the decision-making processes of neural networks, which are well-known black boxes. Today, we talk about TDA, which aims to relate a model’s inference from a specific sample to its training data.

Explainability 56

Explainability Explainable AI Python Neural Network 56

Mlearning.ai

JANUARY 28, 2023

TensorFlow: TensorFlow is an open source library for building neural networks and other deep learning algorithms on top of GPUs. Keras : Keras is a high-level neural network library that makes it easy to develop and deploy deep learning models. How Do I Use These Libraries?

Data Science 52

Data Science Machine Learning ML Python 52

Marktechpost

OCTOBER 18, 2024

Weight averaging, originating from Utans’ work in 1996, has been widely applied in deep neural networks for combining checkpoints, utilizing task-specific information, and parallel training of LLMs. Researchers have explored various approaches to address the challenges of model merging and multitask learning in LLMs.

Large Language Models 60

Large Language Models Neural Network Inference Engine LLM 60

Viso.ai

MAY 24, 2024

This technique combines learning capabilities and logical reasoning from neural networks and symbolic AI. It uses formal languages, like first-order logic, to represent knowledge and an inference engine to draw logical conclusions based on user queries. Extracting information from the patterns learned by neural networks.

Neural Network 52

Neural Network Robotics Automation Explainability 52

Mlearning.ai

JUNE 2, 2023

NNAPI   — The Android Neural Networks API (NNAPI) is an Android C API designed for running computationally intensive operations for machine learning on mobile devices and enables hardware-accelerated inference operations on Android devices. In order to tackle this, the team at Modular developed a modular inference engine.

Inference Engine 52

Inference Engine Python Machine Learning Neural Network 52

Bugra Akyildiz

SEPTEMBER 22, 2024

Skip connections: These are used to facilitate gradient flow in deep SSM architectures, similar to their use in other deep neural networks. It is designed to facilitate research by providing building blocks that can be recombined in custom ways in order to optimise parametric models such as, but not limited to, deep neural networks.

Neural Network 59

Neural Network LLM Large Language Models Data Ingestion 59

deepsense.ai

APRIL 25, 2024

Tech Stack Tech Stack Below, we provide a quick overview of the project, divided into research and inference sites. Methods and Tools Let’s start with the inference engine for the Small Language Model. While we haven’t tested it as an inference engine, it could interest those looking to utilize Gemma models.

Prompt Engineering 52

Prompt Engineering Prompt Engineer Inference Engine LLM 52

Bugra Akyildiz

SEPTEMBER 8, 2024

Netron : Compared to Netron, a popular general-purpose neural network visualization tool, Model Explorer is specifically designed to handle large-scale models effectively. 👷 The LLM Engineer focuses on creating LLM-based applications and deploying them. For additional information about Gemma, see ai.google.dev/gemma.

LLM 52

LLM Large Language Models Neural Network Deep Learning 52

Mlearning.ai

JULY 9, 2023

LLM from a CPU-Optimized (GGML) format: LLaMA.cpp is a C++ library that provides a high-performance inference engine for large language models (LLMs). It is based on the GGML (Graph Neural Network Machine Learning) library, which provides a fast and efficient way to represent and process graphs.

Large Language Models 52

Large Language Models LLM Python Auto-complete 52

Towards AI

JULY 19, 2023

github.com Their core repos consist of SparseML: a toolkit that includes APIs, CLIs, scripts and libraries that apply optimization algorithms such as pruning and quantization to any neural network. DeepSparse: a CPU inference engine for sparse models. Follow their code on GitHub. SparseZoo: a model repo for sparse models.

NLP 98

NLP Neural Network Natural Language Processing BERT 98

Towards AI

JULY 21, 2023

github.com Their core repos consist of SparseML: a toolkit that includes APIs, CLIs, scripts and libraries that apply optimization algorithms such as pruning and quantization to any neural network. DeepSparse: a CPU inference engine for sparse models. Follow their code on GitHub. SparseZoo: a model repo for sparse models.

NLP 52

NLP Neural Network Natural Language Processing BERT 52

Marktechpost

OCTOBER 28, 2024

Deep neural networks, typically fine-tuned foundational models, are widely used in sectors like healthcare, finance, and criminal justice, where biased predictions can have serious societal impacts. Datasets and pre-trained models come with intrinsic biases. If you like our work, you will love our newsletter.

Neural Network 110

Neural Network Inference Engine Machine Learning Automation 110

Marktechpost

OCTOBER 28, 2024

The OCM methodology offers a streamlined approach to estimating covariance by training a neural network to predict the diagonal Hessian, which allows for accurate covariance approximation with minimal computational demands. If you like our work, you will love our newsletter. Don’t Forget to join our 55k+ ML SubReddit.

Neural Network 101

Neural Network Inference Engine AI AI 101