Towards AI

APRIL 27, 2023

Understanding the Principles, Challenges, and Applications of Gradient Descent Image by Author with @MidJourney Introduction to Gradient Descent Gradient descent is a fundamental optimization algorithm used in machine learning and data science to find the optimal values of the parameters in a model.

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Marktechpost

MARCH 14, 2024

The researchers focus on supervised online continual learning, a scenario where a model learns from a continuous stream of examples, adjusting its predictions over time. Leveraging the unique strengths of transformers in in-context learning and their connection to meta-learning, researchers have proposed a novel approach.

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Marktechpost

JULY 24, 2023

The most common training approach for differential privacy in image recognition is differential private stochastic gradient descent (DPSGD). In some model updates, adding noise to the gradients might worsen the objective function values, especially when convergence is imminent. Check out the Paper.

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Marktechpost

MAY 19, 2024

Google AI researchers describe their novel approach to addressing the challenge of generating high-quality synthetic datasets that preserve user privacy, which are essential for training predictive models without compromising sensitive information. All credit for this research goes to the researchers of this project.

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AI Weekly

OCTOBER 26, 2023

listverse.com Research A new method for safety helmet detection based on convolutional neural network Using the designed channel attention module and location attention module, adjacent shallow feature maps and upsampled feature maps are adaptively fused to generate new feature maps with strong semantics and precise location information.

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NYU Center for Data Science

DECEMBER 20, 2022

The NeurIPS conference strives to bolster intellectual exchange and advance research in the areas of artificial intelligence and machine learning. The winning paper explores stochastic gradient descent (SGD). by Meryl Phair

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Marktechpost

MARCH 15, 2024

When conducting stochastic gradient descent-based optimization, they must be more sufficient to accommodate these vast parameters and their associated optimizer states. However, this approach introduces prohibitive costs for most academic researchers, who always have a limited budget for many high-end GPU servers.

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Viso.ai

APRIL 29, 2024

However, a team of researchers were driven to prove that Deep Neural Architectures were the future, and succeeded in it; AlexNet exploded the interest in deep learning post-2012. CUDA Architecture – source Vanishing Gradient Problem: Deep Networks faced a vanishing gradient problem. What is ImageNet?

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Marktechpost

SEPTEMBER 8, 2023

When the vector collapses to 1, SAD becomes a state-of-the-art (SOTA) collaborative filtering model; in this research, we permit its value to be determined from data. The team presents a quick group coordinate descent method for SAD parameter estimation. All Credit For This Research Goes To the Researchers on This Project.

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Marktechpost

AUGUST 21, 2023

The researchers have used two-layered cascaded metasurfaces to demonstrate the approach by using handwritten digits and fashion as inputs, respectively. The training phase involves optimizing the parameters of the metasurface components using stochastic gradient descent and error backpropagation methods.

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Marktechpost

SEPTEMBER 21, 2023

This allows OmniQuant to be optimized efficiently using a simple stochastic gradient descent (SGD) algorithm. All Credit For This Research Goes To the Researchers on This Project. It is a practical model as it’s quite easy to implement even on a single GPU. Check out the Paper and Github link.

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Towards AI

SEPTEMBER 10, 2023

course is led by Jeremy Howard, a founding researcher of Fast.AI. Graph by author A couple of other mathematical concepts that are used: SGD (Stochastic Gradient Descent) — an optimisation approach based on gradient calculation. It’s called Gradient Accumulation. About the course The Fast.AI

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Google Research AI blog

MAY 19, 2023

Posted by Natalia Ponomareva and Alex Kurakin, Staff Software Engineers, Google Research Large machine learning (ML) models are ubiquitous in modern applications: from spam filters to recommender systems and virtual assistants. Additionally, other hyperparameters like the learning rate should be re-tuned to account for noisy gradient updates.

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Flipboard

APRIL 17, 2023

We use the simple stochastic gradient update as our optimizer. It uses the jax.value_and_grad function from the JAX library, which takes a function as an argument and returns the value of the function and its gradient with respect to its inputs. We use the simple stochastic gradient update as our optimizer.

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Heartbeat

FEBRUARY 21, 2024

However, this technology offers important innovations and solutions for many fields and is a rapidly developing research and application area. The optimizer calculates gradients (slopes) using backpropagation and updates parameters using these gradients. Gradients ensure that parameters are updated in the right direction.

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Google Research AI blog

DECEMBER 7, 2022

Posted by Krishna Giri Narra, Software Engineer, Google, and Chiyuan Zhang, Research Scientist, Google Research Ad technology providers widely use machine learning (ML) models to predict and present users with the most relevant ads, and to measure the effectiveness of those ads. accuracy) with large computational overheads.

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BAIR

JULY 10, 2022

In cooperative multi-agent reinforcement learning (MARL), due to its on-policy nature, policy gradient (PG) methods are typically believed to be less sample efficient than value decomposition (VD) methods, which are off-policy. Moreover, stochastic gradient descent can guarantee PG to converge to one of these optima under mild assumptions.

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Google Research AI blog

FEBRUARY 10, 2023

Posted by Vahab Mirrokni, VP and Google Fellow, Google Research (This is Part 5 in our series of posts covering different topical areas of research at Google. Google Research has been at the forefront of this effort, developing many innovations from privacy-safe recommendation systems to scalable solutions for large-scale ML.

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Shreyansh Singh

DECEMBER 17, 2021

FedSGD Stochastic Gradient Descent (SGD) had shown great results in deep learning. So, as a baseline, the researchers decided to base the Federated Learning training algorithm on SGD as well. This corresponds to a full-batch (non-stochastic) gradient descent. FedAvg We saw FedSGD.

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Pickl AI

JULY 7, 2023

Say hello to Gradient Boosting Algorithm! What is Gradient Boosting? Gradient boosting is not just your regular algorithm; it’s a functional gradient algorithm that works wonders in the world of machine learning. For this, it chooses a function with weak hypothesis or negative gradient.

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Google Research AI blog

JULY 23, 2023

Posted by Cat Armato, Program Manager, Google Groups across Google actively pursue research in the field of machine learning (ML), ranging from theory and application. As a leader in ML research, Google has a strong presence at this year’s conference with over 120 accepted papers and active involvement in a number of workshops and tutorials.

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Mlearning.ai

JULY 11, 2023

Also, familiarize yourself with optimization algorithms like stochastic gradient descent (SGD), Adam, and RMSprop. Stay updated by following influential researchers, attending conferences, and reading research papers. Learn how to fine-tune model parameters effectively.

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The MLOps Blog

FEBRUARY 28, 2023

When model training is pipelined across multiple machines, there is a delay that happens between when the forward computation on data occurs and when the gradients based on that computation are backpropagated to update the model weights. During training, data and gradients are communicated between machines.

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AWS Machine Learning Blog

JULY 13, 2023

With data parallelism, data is partitioned among the compute nodes, and each node computes the gradients based on their partition and updates the model. Some researchers proposed that configuring a larger mini-batch may lead to gradients with less stochasticity. Another way can be to use an AllReduce algorithm.

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AssemblyAI

MAY 3, 2023

Effective methods allowing for better control, or steerability , of large-scale AI systems are currently in extremely high demand in the world of AI research. To address this issue, researchers have developed a simple strategy called Instruction Tuning. RLHF is perhaps the most popular of the current methods.

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Heartbeat

AUGUST 21, 2023

GoogLeNet: is a highly optimized CNN architecture developed by researchers at Google in 2014. ResNet is a deep CNN architecture developed by Kaiming He and his colleagues at Microsoft Research in 2015. We have also covered best practices for using CNNs and discussed some of the future directions for CNN research.

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PyImageSearch

DECEMBER 5, 2022

Every operator is automatically differentiated and is waiting to be used in an optimization algorithm (like stochastic gradient descent). Note : Let us recall that in a neural network, we compute gradients with respect to the parameters (weights and biases) and not the inputs (the image). This is illustrated in Figure 1.

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Google Research AI blog

DECEMBER 20, 2022

Posted by Pritish Kamath and Pasin Manurangsi, Research Scientists, Google Research Differential privacy (DP) is an approach that enables data analytics and machine learning (ML) with a mathematical guarantee on the privacy of user data. A notable example is the differentially-private stochastic gradient descent (DP-SGD) algorithm.

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Flipboard

FEBRUARY 5, 2023

One system in particular, called Birdbrain, is continuously improving the learner’s experience with algorithms based on decades of research in educational psychology, combined with recent advances in machine learning. But behind the scenes, sophisticated artificial-intelligence (AI) systems are at work.

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Google Research AI blog

MARCH 28, 2023

Posted by Harsh Mehta, Software Engineer, and Walid Krichene, Research Scientist, Google Research Large deep learning models are becoming the workhorse of a variety of critical machine learning (ML) tasks. The most popular method for DP training in deep learning is differentially private stochastic gradient descent (DP-SGD).

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Heartbeat

APRIL 4, 2023

Many people trace the modern development of NST to the detailed research work by Gatys et al. The research shows that it is possible to create an entirely new image by modifying the convolutional neural network weights of a content image to match those of a style image. We will use the Adam optimizer to minimize the total loss.

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Google Research AI blog

NOVEMBER 28, 2022

NeurIPS 2022 will be held as a hybrid event, in person in New Orleans, LA with some virtual attendance options, and includes invited talks, demonstrations and presentations of some of the latest in machine learning research. You can learn more about our work being presented in the list below (Google affiliations highlighted in bold ).

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Pickl AI

SEPTEMBER 18, 2023

Boltzmann Machines: Boltzmann Machines are a type of stochastic neural network with both visible and hidden units. They adjust their parameters (weights and biases) through optimization algorithms like stochastic gradient descent (SGD) to minimize the difference between generated data and real data.

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Bugra Akyildiz

FEBRUARY 24, 2024

Articles Microsoft Research introduces Phi-2 , a surprisingly powerful small language model (SLM) with only 2.7 Increased Democratization: Smaller models like Phi-2 reduce barriers to entry, allowing more developers and researchers to explore the power of large language models. billion parameters.

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Google Research AI blog

FEBRUARY 7, 2023

Posted by Sanjiv Kumar, VP and Google Fellow, Google Research (This is Part 4 in our series of posts covering different topical areas of research at Google. Our research this year has looked at tailoring distillation to specific settings and formally studying the factors that govern the success of distillation.

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Mlearning.ai

MARCH 15, 2023

During training, data collected from edge devices (clients) are divided into batches, where the model learns from these batches through a Stochastic Gradient Descent (SGD) methodology. FL is still considered an emerging topic and ongoing research is taking place to develop new techniques to make them more effective and reliable.

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Heartbeat

JANUARY 29, 2024

Photo by Brett Jordan on Unsplash In the ever-evolving landscape of artificial intelligence and machine learning, researchers and practitioners continuously seek to elevate the capabilities of intelligent systems. Also, backpropagation computes the gradients of the model's parameters concerning the loss.

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