All relaxations are optimized via our Lagrangian framework. All code was implemented using PyTorch, and optimized using L-BFGS. On the right, the difference framework is used to achieve equality of opportunity on COMP AS. We set the initial learning rate 0.1, which was Here we define equality of opportunity on false negative rates, i.e. predicting that someone Setting s = b, however, causes the linear relaxation to degenerate. For our deep learning experiments, we used the approach of Sec.

On-device continual learning (CL) requires the co-optimization of model accuracy and resource efficiency to be practical. This is extremely challenging because it must preserve accuracy while learning new tasks with continuously drifting data and maintain both high energy and memory efficiency to be deployable on real-world devices. Typically, a CL method leverages one of two types of backbone networks: CNN or ViT. It is commonly believed that CNN-based CL excels in resource efficiency, whereas ViT-based CL is superior in model performance, making each option attractive only for a single aspect. In this paper, we revisit this comparison while embracing powerful pre-trained ViT models of various sizes, including ViT-Ti (5.8M parameters). Our detailed analysis reveals that many practical options exist today for making ViT-based methods more suitable for on-device CL, even when accuracy, energy, and memory are all considered. To further expand this impact, we introduce REP, which improves resource efficiency specifically targeting prompt-based rehearsal-free methods. Our key focus is on avoiding catastrophic trade-offs with accuracy while trimming computational and memory costs throughout the training process. We achieve this by exploiting swift prompt selection that enhances input data using a carefully provisioned model, and by developing two novel algorithms-adaptive token merging (AToM) and adaptive layer dropping (ALD)-that optimize the prompt updating stage. In particular, AToM and ALD perform selective skipping across the data and model-layer dimensions without compromising task-specific features in vision transformer models. Extensive experiments on three image classification datasets validate REP's superior resource efficiency over current state-of-the-art methods.

Reinforcement learning has been demonstrated to outperform even the best humans in complex domains like video games. However, running reinforcement learning experiments on the required scale for autonomous driving is extremely difficult. Building a large scale reinforcement learning system and distributing it across many GPUs is challenging. Gathering experience during training on real world vehicles is prohibitive from a safety and scalability perspective. Therefore, an efficient and realistic driving simulator is required that uses a large amount of data from real-world driving. We bring these capabilities together and conduct large-scale reinforcement learning experiments for autonomous driving. We demonstrate that our policy performance improves with increasing scale. Our best performing policy reduces the failure rate by 64% while improving the rate of driving progress by 25% compared to the policies produced by state-of-the-art machine learning for autonomous driving.

Efficient identification of people and objects, segmentation of regions of interest and extraction of relevant data in images, texts, audios and videos are evolving considerably in these past years, which deep learning methods, combined with recent improvements in computational resources, contributed greatly for this achievement. Although its outstanding potential, development of efficient architectures and modules requires expert knowledge and amount of resource time available. In this paper, we propose an evolutionary-based neural architecture search approach for efficient discovery of convolutional models in a dynamic search space, within only 24 GPU hours. With its efficient search environment and phenotype representation, Gene Expression Programming is adapted for network's cell generation. Despite having limited GPU resource time and broad search space, our proposal achieved similar state-of-the-art to manually-designed convolutional networks and also NAS-generated ones, even beating similar constrained evolutionary-based NAS works. The best cells in different runs achieved stable results, with a mean error of 2.82% in CIFAR-10 dataset (which the best model achieved an error of 2.67%) and 18.83% for CIFAR-100 (best model with 18.16%). For ImageNet in the mobile setting, our best model achieved top-1 and top-5 errors of 29.51% and 10.37%, respectively. Although evolutionary-based NAS works were reported to require a considerable amount of GPU time for architecture search, our approach obtained promising results in little time, encouraging further experiments in evolutionary-based NAS, for search and network representation improvements.

" Naruto, an anime that teaches about many profound things like no matter how bad life puts you down, you got to get back up and move forward" as said by one of my stoned friends. He was high, but I couldn't refute him not because I was stoned too but because I agreed with him completely. A little context about how I got the idea, I am currently a visiting researcher in Australia at UTS. I am working on research in ML, XR and quantum domains. So, being the geek I am, I marveled at the possible implications of making a naruto game in AR where you make the signs and a Jutsu follows up. Think: When you do the Dog, hare, Dragon, boar, tiger signs, a fire dragon comes into existence and crashes onto the enemy..killing him if he doesn't counter-attack with a water or earth Jutsu.

I helped work on a thing last weekend that I can't write about, yet, and then last week I found my way to San Jose for Nvidia's GPU Technology Conference, and fine, all right, OK, I'm convinced: Now that the smartphone boom is plateauing, AI/deep learning is the new coal face of technology -- and, at least for now, Nvidia bestrides it like many parallel colossi. I use the metaphor "coal face" advisedly. It's the place where advances are being made, where the most value is being created … but it's also a messy business, often with little visibility, with many ways to go terribly wrong. Neural networks are still more applied science than they are engineering, although it's beginning to move along that spectrum. The Nvidia GPU conference featured a sizable zone of scientific posters exploring the cutting edge of GPU usage, something you don't see at a lot of tech conferences.