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 105

Neural Network Inference Engine Large Language Models AI 105

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. Also, don’t forget to follow us on Twitter and join our Telegram Channel and LinkedIn Gr oup.

Neural Network 105

Neural Network Inference Engine Machine Learning ML 105

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 105

Neural Network Inference Engine NLP ML 105

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 115

Neural Network Inference Engine Machine Learning Automation 115

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 105

Deep Learning Neural Network Robotics Inference Engine 105

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 116

Large Language Models Neural Network Inference Engine AI 116

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 105

Categorization Neural Network Inference Engine AI 105

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 69

Machine Learning Neural Network Deep Learning Inference Engine 69

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 64

Machine Learning Neural Network Inference Engine Natural Language Processing 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.

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Explainable AI Explainability Python Neural Network 59

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 64

Large Language Models Neural Network Inference Engine LLM 64

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. By 2021, they had over 20 billion block rows in Postgres, growing to over 200 billion blocks by 2024. LayerNorm) to stabilize training.

Neural Network 59

Neural Network LLM Large Language Models Data Ingestion 59

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

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. Also, don’t forget to follow us on Twitter and join our Telegram Channel and LinkedIn Gr oup.

Neural Network 104

Neural Network Inference Engine AI AI 104