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In this tutorial, we explore Equinox, a lightweight and elegant neural network library built on JAX, and show how to use it. We begin by understanding how eqx.Module treats models as PyTrees, which makes parameter handling, transformation, and serialization feel simple and explicit. As we move forward, we work through static fields, filtered transformations such as filter_jit and filter_grad, PyTree manipulation utilities, stateful layers such as BatchNorm, and a complete end-to-end training workflow for a toy regression problem. […]

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We maintain an open-source catalog of cloud GPU offerings (skypilot-catalog, Apache 2.0). It auto-fetches pricing from 20+ cloud APIs every 7 hours. We made it browsable – 50 GPU models, 2K+ offerings, on-demand and spot pricing, historical trends. A few other GPU comparison tools already use our catalog as their data source. Figured we’d make the raw data visible to everyone. submitted by /u/Shot-Patience-9874 [link] [comments]

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Google’s newest TPUs are faster and cheaper than the previous versions. But the company is still embracing Nvidia in its cloud — for now.

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A conversation with leadership development experts Amy Jen Su and Muriel M. Wilkins about what it takes to be seen as a leader—whether you have the top job or not.

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My paper got rejected from an imaging venue (A*) because it lacked clinical validation and was more “NLP suited”. I’m very disappointed by the decision as the paper had strong methods and key findings suited to the specific venue. I’m thinking of EMNLP next, but I feel it is too NLP and my paper for sure will be lost. But I see an EMNLP workshop very suited to the paper. Are such workshops especially at such conferences any […]

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Federated learning (FL) enables collaborative model training without sharing raw data; however, the presence of noisy labels across distributed clients can severely degrade the learning performance. In this paper, we propose FedSIR, a multi-stage framework for robust FL under noisy labels. Different from existing approaches that mainly rely on designing noise-tolerant loss functions or exploiting loss dynamics during training, our method leverages the spectral structure of client feature representations to identify and mitigate label noise. Our framework consists […]

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Language models trained on natural text learn to represent numbers using periodic features with dominant periods at $T=2, 5, 10$. In this paper, we identify a two-tiered hierarchy of these features: while Transformers, Linear RNNs, LSTMs, and classical word embeddings trained in different ways all learn features that have period-$T$ spikes in the Fourier domain, only some learn geometrically separable features that can be used to linearly classify a number mod-$T$. To explain this incongruity, we prove that […]

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submitted by /u/Old-Raspberry-3266 [link] [comments]

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