Marktechpost

JUNE 22, 2024

Neural networks, despite their theoretical capability to fit training sets with as many samples as they have parameters, often fall short in practice due to limitations in training procedures. Key technical aspects include the use of various neural network architectures (MLPs, CNNs, ViTs) and optimizers (SGD, Adam, AdamW, Shampoo).

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Marktechpost

FEBRUARY 28, 2024

Parameter generation, distinct from visual generation, aims to create neural network parameters for task performance. Researchers from the National University of Singapore, University of California, Berkeley, and Meta AI Research have proposed neural network diffusion , a novel approach to parameter generation.

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Neural Network Deep Learning AI Researcher AI Research 108

Unite.AI

MARCH 24, 2024

Central to this advancement in NLP is the development of artificial neural networks, which draw inspiration from the biological neurons in the human brain. These networks emulate the way human neurons transmit electrical signals, processing information through interconnected nodes.

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Neural Network Robotics Natural Language Processing NLP 354

Unite.AI

JUNE 15, 2024

The Harvard researchers worked closely with the DeepMind team to build a biomechanically realistic digital model of a rat. The neural network was trained to use inverse dynamics models, which are believed to be employed by our brains for guiding movement.

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Marktechpost

FEBRUARY 4, 2024

Meta-learning, a burgeoning field in AI research, has made significant strides in training neural networks to adapt swiftly to new tasks with minimal data. This technique centers on exposing neural networks to diverse tasks, thereby cultivating versatile representations crucial for general problem-solving.

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Marktechpost

JANUARY 2, 2024

Without this framework, comprehending the model’s structure becomes cumbersome for AI researchers. This tool functions as a viewer specifically designed for neural networks, supporting frameworks like TensorFlow Lite, ONNX, Caffe, Keras, etc.

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Marktechpost

MARCH 14, 2024

In order to address this, a team of researchers has focussed on users’ current musical and podcast interests and and has presented a new recommendation engine known as 2T-HGNN. In this architecture, a Two Tower (2T) model and a Heterogeneous Graph Neural Network (HGNN) have been combined into a single stack.

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Marktechpost

OCTOBER 16, 2023

In a recent paper, “Towards Monosemanticity: Decomposing Language Models With Dictionary Learning,” researchers have addressed the challenge of understanding complex neural networks, specifically language models, which are increasingly being used in various applications. Join our AI Channel on Whatsapp.

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Marktechpost

DECEMBER 7, 2023

Also, don’t forget to join our 33k+ ML SubReddit , 41k+ Facebook Community, Discord Channel , and Email Newsletter , where we share the latest AI research news, cool AI projects, and more. If you like our work, you will love our newsletter.

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Marktechpost

SEPTEMBER 3, 2023

Credit assignment in neural networks for correcting global output mistakes has been determined using many synaptic plasticity rules in natural neural networks. Methods of biological neuromodulation have inspired several plasticity algorithms in models of neural networks.

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Marktechpost

MARCH 29, 2024

In technological advancement, developing Neural Network Language Models (NNLMs) for on-device Virtual Assistants (VAs) represents a significant leap forward. Researchers from AppTek GmbH and Apple tackle these issues by pioneering a “World English” NNLM that amalgamates various dialects of English into a single, cohesive model.

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Marktechpost

OCTOBER 22, 2023

In the pursuit of replicating the complex workings of the human sensory systems, researchers in neuroscience and artificial intelligence face a persistent challenge: the disparity in invariances between computational models and human perception. Join our AI Channel on Whatsapp. If you like our work, you will love our newsletter.

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Marktechpost

DECEMBER 21, 2023

ReLoRA accomplishes a high-rank update, delivering a performance akin to conventional neural network training. link] Scaling laws have been identified, demonstrating a strong power-law dependence between network size and performance across different modalities, supporting overparameterization and resource-intensive neural networks.

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Marktechpost

NOVEMBER 25, 2023

Also, don’t forget to join our 33k+ ML SubReddit , 41k+ Facebook Community, Discord Channel , and Email Newsletter , where we share the latest AI research news, cool AI projects, and more. If you like our work, you will love our newsletter.

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Marktechpost

NOVEMBER 25, 2023

Addressing this, Jason Eshraghian from UC Santa Cruz developed snnTorch, an open-source Python library implementing spiking neural networks, drawing inspiration from the brain’s remarkable efficiency in processing data. Traditional neural networks lack the elegance of the brain’s processing mechanisms.

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Neural Network Python Deep Learning Artificial Intelligence 124

Marktechpost

OCTOBER 1, 2023

Researchers have recently developed Temporal Graph Neural Networks (TGNNs) to take advantage of temporal information in dynamic graphs, building on the success of Graph Neural Networks (GNNs) in learning static graph representation. If you like our work, you will love our newsletter.

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Marktechpost

NOVEMBER 7, 2023

Neural networks have become indispensable tools in various fields, demonstrating exceptional capabilities in image recognition, natural language processing, and predictive analytics. The sum of these vectors is then passed to the next layer, creating a sparse and discrete bottleneck within the network.

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Neural Network Natural Language Processing Machine Learning AI 117

Marktechpost

SEPTEMBER 4, 2023

Lately, there have been significant strides in applying deep neural networks to the search field in machine learning, with a specific emphasis on representation learning within the bi-encoder architecture. If you like our work, you will love our newsletter.

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Marktechpost

SEPTEMBER 15, 2023

The traditional theory of how neural networks learn and generalize is put to the test by the occurrence of grokking in neural networks. This behavior is basically grokking in neural networks. Generalizing Solution: With this approach, the neural network is well-suited to generalizing to new data.

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Marktechpost

OCTOBER 8, 2023

Yes, the field of study is called Neural networks. Researchers at the University of Copenhagen present a graph neural network type of encoding in which the growth of a policy network is controlled by another network running in each neuron. They call it a Neural Developmental Program (NDP).

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Marktechpost

JULY 7, 2024

Artificial neural networks (ANNs) traditionally lack the adaptability and plasticity seen in biological neural networks. Overall, LNDPs demonstrated superior adaptation speed and learning efficiency, highlighting their potential for developing adaptable and self-organizing AI systems. Check out the Paper.

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Marktechpost

MARCH 4, 2024

Efficiency of Large Language Models (LLMs) is a focal point for researchers in AI. A groundbreaking study by Qualcomm AI Research introduces a method known as GPTVQ, which leverages vector quantization (VQ) to enhance the size-accuracy trade-off in neural network quantization significantly.

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Marktechpost

DECEMBER 23, 2023

Deep Neural Networks (DNNs) represent a powerful subset of artificial neural networks (ANNs) designed to model complex patterns and correlations within data. These sophisticated networks consist of multiple layers of interconnected nodes, enabling them to learn intricate hierarchical representations.

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Marktechpost

DECEMBER 13, 2023

Techniques include treating LLMs as task predictors, feature encoders for Graph Neural Networks (GNNs), or aligners with GNNs, depending on their function and interaction with GNNs. The post This AI Research Shares a Comprehensive Overview of Large Language Models (LLMs) on Graphs appeared first on MarkTechPost.

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Marktechpost

JANUARY 3, 2024

Also, don’t forget to join our 35k+ ML SubReddit , 41k+ Facebook Community, Discord Channel , LinkedIn Gr oup , and Email Newsletter , where we share the latest AI research news, cool AI projects, and more. If you like our work, you will love our newsletter.

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Marktechpost

OCTOBER 13, 2023

In the realm of deep learning, the challenge of developing efficient deep neural network (DNN) models that combine high performance with minimal latency across a variety of devices remains. Join our AI Channel on Whatsapp. If you like our work, you will love our newsletter. We are also on WhatsApp.

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Marktechpost

SEPTEMBER 15, 2023

These factors include the availability of huge amounts of data, improvements in computer power, and breakthroughs in the design of neural networks. The post This AI Research Introduces AstroLLaMA: A 7B Parameter Model Fine-Tuned from LLaMA-2 Using Over 300K Astronomy Abstracts From ArXiv appeared first on MarkTechPost.

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Marktechpost

DECEMBER 15, 2023

Complex tasks like text or picture synthesis, segmentation, and classification are being successfully handled with the help of neural networks. However, it can take days or weeks to obtain adequate results from neural network training due to its computing demands. If you like our work, you will love our newsletter.

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Marktechpost

JANUARY 14, 2024

These intricate systems use neural networks to interpret and respond to linguistic inputs. Don’t Forget to join our Telegram Channel The post Google AI Research Introduces Patchscopes: A Revolutionary AI Framework for Decoding and Enhancing the Interpretability of Large Language Models appeared first on MarkTechPost.

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Marktechpost

DECEMBER 30, 2023

While PDE discovery seeks to determine a PDE’s coefficients from data, PDE solvers use neural networks to approximate a known PDE’s solution. In recent research, researchers from the University of Cambridge and Cornell University have provided a step-by-step mathematical guide to operator learning.

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Marktechpost

DECEMBER 17, 2023

Neural networks, the marvels of modern computation, encounter a significant hurdle when confronted with tabular data featuring heterogeneous columns. The essence of this challenge lies in the networks’ inability to handle diverse data structures within these tables effectively.

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Marktechpost

OCTOBER 28, 2023

To address this issue, NYU researchers have introduced an “interpretable-by-design” approach that not only ensures accurate predictive outcomes but also provides insights into the underlying biological processes, specifically RNA splicing. Join our AI Channel on Whatsapp. If you like our work, you will love our newsletter.

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Marktechpost

AUGUST 31, 2024

Deep neural network training can be sped up by Fully Quantised Training (FQT), which transforms activations, weights, and gradients into lower precision formats. They have experimented with their approach by optimizing popular neural network models, like VGGNet-16 and ResNet-18, using various datasets.

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Marktechpost

DECEMBER 26, 2023

Conventional methods in this field have heavily relied on deep neural networks (DNNs) due to their exceptional ability to model complex patterns. Reduction of model complexity through neural network sparsification. Gradual decrease of nonlinearity in neural models, optimizing them for efficient use in automatic control.

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Marktechpost

JANUARY 13, 2024

Traditionally, approaches to tackle this issue have leaned on two architectural types: transformer-based models inspired by Large Language Models (LLMs) and Graph Neural Networks (GNNs). These methodologies have been instrumental in encoding text, layout, and image features to improve document interpretation.

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AI Researcher AI Research Large Language Models Neural Network 133

Unite.AI

NOVEMBER 25, 2023

By integrating these constraints, the AI not only mirrors aspects of human intelligence but also unravels the intricate balance between resource expenditure and information processing efficiency. These limitations are not hindrances but are instrumental in shaping its structure and function.

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Marktechpost

NOVEMBER 26, 2023

Neural network architectures, particularly created and trained for few-shot knowledge the ability to learn a desired behavior from a small number of examples, were the first to exhibit this capability. Since then, a substantial amount of research has examined or documented instances of ICL.

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