Motivation: Pathway enrichment analysis is widely used to interpret gene expression data. Standard approaches, such as GSEA, rely on predefined phenotypic labels and pairwise comparisons, which limits their applicability in unsupervised settings. Existing unsupervised extensions, including single-sample methods, provide pathway-level summaries but primarily capture linear relationships and do not explicitly model gene-pathway associations. More recently, deep learning models have been explored to capture non-linear transcriptomic structure. However, their interpretation has typically relied on generic explainable AI (XAI) techniques […]

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I am a Computer Vision and ML engineer with over five years of experience and a research based Masters degree. A few months ago I left a well paying remote role because the work environment and micromanagement were seriously affecting my mental health. At the time I believed stepping away was the right decision for my sanity. It has now been around three months and I am barely getting any recruiter screens let alone technical interviews. The lack […]

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In a bear market, survival is the prerequisite for catching the next wave. However, the ability to put capital to work while surviving is what truly separates the winners from the rest. The most common dilemma during volatile periods is that funds either endure fluctuations with little growth or sacrifice flexibility for interest, making investors slow to respond when opportunities arise. MEXC Earn’s product restructuring addresses this contradiction: through a “Deposit-Earn-Borrow” ecosystem, it integrates earnings, trading, and turnover […]

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arXiv:2601.04212v1 Announce Type: new Abstract: Large language models (LLMs) have exhibited remarkable proficiency in generating high-quality text; however, their propensity for producing hallucinations poses a significant challenge for their deployment in security-critical domains. In this work, we present TrueBrief, an end-to-end framework specifically designed to enhance the faithfulness of small LLMs (SLMs) primarily for the task of text summarization through a preference-optimization paradigm. Central to our framework is a data generation module that facilitates controlled hallucination injection to […]

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arXiv:2602.11679v1 Announce Type: new Abstract: We introduce a novel cyclic Markov decision process (MDP) framework for multi-step decision problems with heterogeneous stage-specific dynamics, transitions, and discount factors across the cycle. In this setting, offline learning is challenging: optimizing a policy at any stage shifts the state distributions of subsequent stages, propagating mismatch across the cycle. To address this, we propose a modular structural framework that decomposes the cyclic process into stage-wise sub-problems. While generally applicable, we instantiate this […]

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arXiv:2601.17091v1 Announce Type: new Abstract: ROCKET (RandOm Convolutional KErnel Transform) is a feature extraction algorithm created for Time Series Classification (TSC), published in 2019. It applies convolution with randomly generated kernels on a time series, producing features that can be used to train a linear classifier or regressor like Ridge. At the time of publication, ROCKET was on par with the best state-of-the-art algorithms for TSC in terms of accuracy while being significantly less computationally expensive, making ROCKET […]

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The versatility of exponential families, along with their attendant convexity properties, make them a popular and effective statistical model. A central issue is learning these models in high-dimensions when the optimal parameter vector is sparse. This work characterizes a certain strong convexity property of general exponential families, which allows their generalization ability to be quantified. In particular, we show how this property can be used to analyze generic exponential families under L1 regularization.

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How are you, hacker? 🪐Want to know what’s trending right now?: The Techbeat by HackerNoon has got you covered with fresh content from our trending stories of the day! Set email preference here. ## AI Doesn’t Mean the End of Work for Us By @bernard [ 4 Min read ] I believe that AI’s impact and future pathways are overstated because human nature is ignored in such statements. Read More. Claude Code Launches Teleport Workflow: Start Anywhere, Continue […]

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Introduction Disclaimer: This article is a cross post from Pytorch Medium Blog Post. One of the most exciting advancements, that has pushed the frontier of the Artificial Intelligence (AI) in recent years, is Deep Reinforcement Learning (DRL). DRL belongs to the family of machine learning algorithms. It assumes that intelligent machines can learn from their actions similar to the way humans learn from experience. Over the recent years we could witness some impressive real-world applications of DRL. The […]

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