arXiv:2406.03628v3 Announce Type: replace Abstract: Imbalanced classification and spurious correlation are common challenges in data science and machine learning. Both issues are linked to data imbalance, with certain groups of data samples significantly underrepresented, which in turn would compromise the accuracy, robustness and generalizability of the learned models. Recent advances have proposed leveraging the flexibility and generative capabilities of large language models (LLMs), typically built on transformer architectures, to generate synthetic samples and to augment the observed data. […]

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Are the human-like cognitive abilities of LLMs real or fake? How does information travel through the neural network? Is there hidden knowledge inside an LLM? The post Mechanistic Interpretability: Peeking Inside an LLM appeared first on Towards Data Science.

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arXiv:2602.06175v1 Announce Type: cross Abstract: Expand-and-sparsify representations are a class of theoretical models that capture sparse representation phenomena observed in the sensory systems of many animals. At a high level, these representations map an input $x in mathbb{R}^d$ to a much higher dimension $m gg d$ via random linear projections before zeroing out all but the $k ll m$ largest entries. The result is a $k$-sparse vector in ${0,1}^m$. We study the suitability of this representation for two […]

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arXiv:2507.04341v2 Announce Type: replace Abstract: While continuous diffusion models excel in modeling continuous distributions, their application to categorical data has been less effective. Recent work has shown that ratio-matching through score-entropy within a continuous-time discrete Markov chain (CTMC) framework serves as a competitive alternative to autoregressive models in language modeling. To enhance this framework, we first introduce three new theorems concerning the KL divergence between the data and learned distribution. Our results serve as the discrete counterpart to […]

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For most of human history, writing was slow, scarce, and authoritative. To write was to fix meaning in place, and to read was to accept that fixation as a serious act. Digital networks loosened that authority by making text cheap and abundant. Generative AI finishes the job by removing human effort as the primary constraint on producing words. Once that happens, writing can no longer derive its value from the act of production itself. Its value must come […]

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arXiv:2601.12230v1 Announce Type: new Abstract: We study classical-quantum (C-Q) channel resolvability. C-Q channel resolvability has been proved by only random coding in the literature. In our previous study, we proved channel resolvability by deterministic coding, using multiplicative weight update algorithm. We extend this approach to C-Q channels and prove C-Q channel resolvability by deterministic coding, using the matrix multiplicative weight update algorithm. This is the first approach to C-Q channel resolvability using deterministic coding.

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I’m excited to share a major update to sklearn-diagnose – the open-source Python library that acts as an “MRI scanner” for your ML models (https://www.reddit.com/r/MachineLearning/s/EcMRYPVIDX) When I first released sklearn-diagnose, users could generate diagnostic reports to understand why their models were failing. But I kept thinking – what if you could talk to your diagnosis? What if you could ask follow-up questions and drill down into specific issues? Now you can! 🚀 🆕 What’s New: Interactive Diagnostic Chatbot […]

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In this paper, we develop a novel online algorithm based on the Sequential Monte Carlo(SMC) samplers framework for posterior inference in Dirichlet Process Mixtures (DPM). Our method generalizes many sequential importance sampling approaches. It provides a computationally efficient improvement to particle filtering that is less prone to getting stuck in isolated modes. The proposed method is a particular SMC sampler that enables us to design sophisticated clustering update schemes, such as updating past trajectories of the particles in […]

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