The resulting Hummingbird model, suitably prompted, performs various scene understanding tasks without modification while approaching the performance of specialists that have been finetuned for each task.

How do birds stay warm in winter? Puffing up is just the start. Breakthroughs, discoveries, and DIY tips sent six days a week. Then you notice a robin hopping from branch to branch. Nearby, ducks are calmly swimming in the pond and waddling around on the ice.

How do you check a hummingbird for broken bones? Micro-CT scans can reveal hard-to-spot fractures in tiny, injured hummingbirds. Breakthroughs, discoveries, and DIY tips sent six days a week. Like clockwork, ruby-throated hummingbirds () start showing up at wildlife hospitals throughout the eastern United States every spring. The jewel-toned birds are often brought in after crashing into windows or being attacked by domestic cats .

High-speed cameras have revealed how hummingbirds negotiate their way through tiny gaps while in flight, which happens much too quickly for the human eye to properly see. The findings could inform new techniques for flying robots. Hummingbirds feed on nectar and have to fly through tiny gaps in cluttered foliage as they flit from flower to flower. Marc Badger at the University of California, Berkeley, says it was while watching hummingbirds from his window that he decided to investigate how they achieve this. "When a dominant male would come and chase an intruder away, that intruder would fly through a bush," he says.

The self-improving ability of large language models (LLMs), enabled by prompting them to analyze and revise their own outputs, has garnered significant interest in recent research. However, this ability has been shown to be absent and difficult to learn for smaller models, thus widening the performance gap between state-of-the-art LLMs and more cost-effective and faster ones. To reduce this gap, we introduce TriPosT, a training algorithm that endows smaller models with such self-improvement ability, and show that our approach can improve a LLaMA-7b's performance on math and reasoning tasks by up to 7.13%. In contrast to prior work, we achieve this by using the smaller model to interact with LLMs to collect feedback and improvements on its own generations. We then replay this experience to train the small model. Our experiments on four math and reasoning datasets show that the interactive experience of learning from and correcting its own mistakes is crucial for small models to improve their performance.

Spurious Features in Training Data bird feeder graffiti eucalyptus label Benchmark performance of deep learning classifiers alone is not a reliable predictor for the performance of a deployed model. In particular, if the image classifier has picked up spurious features in the training data, its predictions can fail in unexpected ways. In this paper, we develop Hummingbird Freight Car Koala Hard Disc a framework that allows us to systematically identify Images from the web with spurious feature spurious features in large datasets like ImageNet. It is but no class features classified as class below based on our neural PCA components and their visualization. Previous work on spurious features often operates in toy settings or requires costly pixel-wise annotations. In contrast, we work with ImageNet and validate our results by showing that presence of the harmful spurious feature of a class alone is sufficient to trigger the prediction of that class. We introduce the novel dataset "Spurious ImageNet" which allows to measure the reliance of any ImageNet classifier on harmful spurious features. Moreover, we introduce SpuFix as a simple mitigation method to reduce the dependence of any ImageNet classifier on previously identified Hummingbird Freight Car Koala Hard Disc harmful spurious features without requiring additional labels Figure 1: Top: Examples of spurious features found via or retraining of the model. We provide code and data our neural PCA components but not in previous study [61].

Hummingbird compiles trained ML models into tensor computation for faster inference. - GitHub - microsoft/hummingbird: Hummingbird compiles trained ML models into tensor computation for faster inference.