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Tutorials• Aug 12, 2026

Why You Shouldn’t Always Trust LLMs as Judges: Understanding Bias in Automated Evaluation

In the rush to automate evaluation, from grading student code to ranking research papers, we have embraced Large Language Models as judges. They are fast. These units are cheap. They scale. However, at a workshop at DHS 2026, Bhaskarjit Sarmah made a point that stuck with me: “you can’t trust LLM as...

#Analytics Vidhya#Data Science

Research• Aug 12, 2026

Transformer Geometry Observatory TGO-IV: Developmental Topology Observatory

arXiv:2608.09997v1 Announce Type: new Abstract: Transformers have had a profound impact on the world of language processing and computer vision. As efforts to answer the million-dollar question of ``How does a Transformer learn?" have been increasing, existing interpretability studies primarily ana...

#ArXiv#Machine Learning#Academic

Research• Aug 12, 2026

Uncertainty-Aware Ensemble Deep Randomized Neural Networks for Classification

arXiv:2608.10007v1 Announce Type: new Abstract: The current state-of-the-art (SOTA) deep randomized neural networks, such as deep Random Vector Functional Link (dRVFL) and ensemble deep RVFL (edRVFL), treat all training samples uniformly, which limits their robustness and effectiveness when applied...

#ArXiv#Machine Learning#Academic

Research• Aug 12, 2026

MIDAS: Mutual Information Disentanglement with Uncertainty-Aware Fusion for Incomplete Multimodal Sentiment Analysis

arXiv:2608.09986v1 Announce Type: new Abstract: Most existing multimodal sentiment analysis approaches assume access to complete multimodal inputs. However, real-world applications frequently encounter incomplete or corrupted modalities, posing a critical challenge. Although several methods have be...

#ArXiv#Machine Learning#Academic

Research• Aug 12, 2026

Towards Sustainable Artificial Intelligence: A Comprehensive Review and Comparative Analysis of Deep Learning Models' Carbon Footprint

arXiv:2608.09998v1 Announce Type: new Abstract: Artificial Intelligence (AI) and Machine Learning (ML) have become powerful tools for supporting and automating complex human tasks. Despite their benefits, growing attention has been directed toward their environmental implications, primarily due to ...

#ArXiv#Machine Learning#Academic

News• Aug 12, 2026

M5B Daily Recap: 2026-08-11

Daily AI Recap: Aug 11, 2026 Welcome to today's curated briefing of the most important AI developments. 🗞️ Top Stories - Accel closes oversubscribed $550M India fund within weeks, 19 months after its lasthttps://techcrunch.com/2026/08/11/accel-closes-oversubscribed-550m-india-fund-within-weeks-19-months-after-its-last/: The U.S. VC firm still has more than 55% of its previous $650 million India fund available for deployment.... - OpenAI launches ChatGPT desktop app for Linuxhttps://techcrunch.com/2026/08/11/openai-launches-chatgpt-desktop-app-for-linux/: OpenAI is finally bringing a dedicated ChatGPT desktop app to Linux operating systems.... - Google’s Gemini app surges to one billion usershttps://techcrunch.com/2026/08/11/googles-gemini-app-surges-to-one-billion-users/: Gemini is keeping pace with OpenAI’s ChatGPT, which hit 1 billion monthly active users back in June.... - Brad Lightcap, OpenAI’s longtime COO, is leaving to ‘start something new’https://techcrunch.com/2026/08/11/brad-lightcap-openais-longtime-coo-is-leaving-to-start-something-new/: One of OpenAI's longest serving executives is headed out the door, although the longtime COO told staff that he was "excited to help you all advance the mission from a different vantage point."... - AMIE, our research medical AI system, demonstrates real-time clinical video consultation capabilities in a first-of-its-kind study.https://blog.google/innovation-and-ai/models-and-research/google-research/amie-video-consultations/: AMIE promotional video... - Stop Calling the First Significant Day a Winhttps://towardsdatascience.com/stop-calling-the-first-significant-day-a-win/: Checking an A/B test until it crosses p The post Stop Calling the First Significant Day a Win appeared first on Towards Data Science.... - The memory wall: Why your GPUs are sitting idlehttps://www.aiacceleratorinstitute.com/why-your-gpus-are-sitting-idle/: Proven strategies for managing large-scale AI inference workloads through smarter infrastructure design.... - Data Infrastructure Summit Virtual Summit | May 2026https://www.aiacceleratorinstitute.com/data-infrastructure-summit-virtual-summit-may-2026/: Catch on every session from Data Infrastructure Summit, with sessions from the likes of USBank, Betterworks, Meta, ServiceNow and more.... - Why Scaling AI Compute Performance Requires a New Power Architecturehttps://blogs.nvidia.com/blog/800-vdc-power-architecture-ai-factory/: Every new generation of accelerated computing demands more from the infrastructure underneath it — more compute performance, higher rack density and more efficient, scalable power distribution. The bo... - Should AI Developers Make the Switch from Polars to Pandas?https://towardsdatascience.com/should-ai-developers-make-the-switch-from-polars-to-pandas/: Not all Python data libraries are created equal! The post Should AI Developers Make the Switch from Polars to Pandas? appeared first on Towards Data Science.... - The Ultimate Guide to Contributing to Open Source Projectshttps://www.kdnuggets.com/the-ultimate-guide-to-contributing-to-open-source-projects: This guide walks through what contributing to open source projects actually covers, how to pick a project that will actually respond to you, the exact git mechanics, and more.... - Thinking of ACE? We Can Do It with Fewer Tokenshttps://huggingface.co/blog/ibm-research/altk-evolve-sldd: ... - The Budget Split That Explains Itselfhttps://towardsdatascience.com/the-budget-split-that-explains-itself/: How to diversify a budget without losing the shadow prices that explain the result The post The Budget Split That Explains Itself appeared first on Towards Data Science.... - Spotify will label ‘AI Persona’ profiles and exclude their music from recommendationshttps://techcrunch.com/2026/08/11/spotify-will-label-ai-persona-profiles-and-exclude-their-music-from-recommendations/: Spotify is introducing “AI Persona” labels for artist profiles that represent AI-generated identities and will exclude their music from editorial, algorithmic, and personalized recommendations by defa... - Claude Now Watermarks Everything It Makeshttps://www.analyticsvidhya.com/blog/2026/08/claude-ai-watermarking-explained/: First, pick the line that applies to you. Since August 2nd, 2026, Claude marks all content during generation. For instance, text receives a hidden watermark, while files receive a signature. Anthropic... - Flow-by-Flow:Content-Judgment Bypass for Governing AI Output in High-Loss Domainshttps://arxiv.org/abs/2608.07474: arXiv:2608.07474v1 Announce Type: new Abstract: Prior work showed that human-in-the-loop oversight becomes structurally untenable in high-loss domains when AI output velocity V exceeds human cognitiv... - Towards an Argumentative Foundation for Evaluative AIhttps://arxiv.org/abs/2608.07473: arXiv:2608.07473v1 Announce Type: new Abstract: Evaluative AI EAI has been recently proposed as a way to support human decision-making, not by producing a single recommendation, but by presenting c... - Tracing sources of epistemic uncertainty in deep learning predictions: homo- and hetero-scedastic linearized estimatorshttps://arxiv.org/abs/2608.07630: arXiv:2608.07630v1 Announce Type: new Abstract: We adapt two classical statistical estimators for quantifying uncertainty to modern deep learning, in order to provide clearer insights into uncertaint... - OpenAI reportedly completed a $7 billion employee tender offerhttps://techcrunch.com/2026/08/10/openai-reportedly-completed-a-7-billion-employee-tender-offer/: San Francisco's housing market is in trouble again.... - Companies That Buy and Sell Your Data Are Not Following California’s Strict Privacy Lawshttps://hai.stanford.edu/news/companies-that-buy-and-sell-your-data-are-not-following-californias-strict-privacy-laws: A new Stanford study shows data brokers are making it difficult for consumers to submit privacy requests and failing to report how many privacy requests they receive.... 🛠️ Featured Tools - Application of Artificial Intelligence for Fraudulent Banking Operations Recognitionhttps://arxiv.org/abs/2608.07471: arXiv:2608.07471v1 Announce Type: new Abstract: This study considers the task of applying artificial intelligence to recognize bank fraud. In recent ... - Data-Driven Fire-Zone Segmentation for Improved Short-Term Wildfire Predictionhttps://arxiv.org/abs/2608.07472: arXiv:2608.07472v1 Announce Type: new Abstract: Wildfire prediction models typically discretize study areas into uniform grids, ignoring the heteroge... - Evolving Safety Landscape of Multi-modal Large Language Models: A Survey of Emerging Threats and Safeguardshttps://arxiv.org/abs/2608.07535: arXiv:2608.07535v1 Announce Type: new Abstract: Multi-modal large language models MLLMs integrate heterogeneous modalities through modality alignme... - SkillConsist: Detecting Inconsistencies in Agent Skills via Bidirectional Graph Alignmenthttps://arxiv.org/abs/2608.07639: arXiv:2608.07639v1 Announce Type: new Abstract: Agent Skills provide reusable capabilities to LLM agents. Agent Skill inconsistencies can expose undi... - Determinization in Structure Theories: A Unified Framework via Closure, Comparability, and Joint Admissibilityhttps://arxiv.org/abs/2608.07476: arXiv:2608.07476v1 Announce Type: new Abstract: We develop a formal framework for constructing canonical interpretations from plural structure theori... - Emotion in an active inference model of human drivinghttps://arxiv.org/abs/2608.07480: arXiv:2608.07480v1 Announce Type: new Abstract: Active inference has emerged as a principled framework for modeling adaptive behavior by balancing go... - Training Variable Long Sequences with Data-Centric Parallelhttps://arxiv.org/abs/2608.07524: arXiv:2608.07524v1 Announce Type: new Abstract: Training deep learning models on variable long sequences poses significant computational challenges. ... - Implementing a MiniMax-H3 Multimodal Video and Audio Generation Pipeline with ComfyUI APIshttps://www.marktechpost.com/2026/08/10/implementing-a-minimax-h3-multimodal-video-and-audio-generation-pipeline-with-comfyui-apis/: In this comprehensive guide, we demonstrate how to implement a complete, programmable MiniMax-H3 multimodal generation pipeline. By leveraging ComfyUI... - webAI Releases TwIL-LM: A 1.7B and 3B Formal-Logic Model Family for Autoformalization on Local Hardwarehttps://www.marktechpost.com/2026/08/10/webai-releases-twil-lm-a-1-7b-and-3b-formal-logic-model-family-for-autoformalization-on-local-hardware/: webAI has released TwIL-LM, a family of formal-logic models at 1.7B and 3B parameters that translate English into first-order logic and check whether ... - A Home for Personal Contexthttps://www.oreilly.com/radar/a-home-for-personal-context/: Every agent I use is building a model of me. Claude has learned how I like my prose. ChatGPT remembers what I’m working on. I don’t mind this—every pe... - Anthropic says it will watermark text generated by its AI modelshttps://techcrunch.com/2026/08/11/anthropic-says-it-will-watermark-text-generated-by-its-ai-models/: Anthropic will extend support for watermarking AI generations for older models as well.... - Can a Local LLM Run My AI Assistant?https://towardsdatascience.com/can-a-local-llm-run-my-ai-assistant/: I replayed the same 27 real production tasks through two local models, one hardware upgrade apart, to find out what it actually takes to replace Claud... - 3 Visual Proofs of the Central Limit Theorem to Build Your Intuitionhttps://www.kdnuggets.com/3-visual-proofs-of-the-central-limit-theorem-to-build-your-intuition: To build your intuition, this article shows three visual proofs that the classic bell curve appears in myriad situations.... - 7 Async Patterns for Running Agents Concurrently in Pythonhttps://machinelearningmastery.com/7-async-patterns-for-running-agents-concurrently-in-python/: In this article, you will learn seven async patterns for running AI agents concurrently in Python, what each pattern is suited for, and the production... - NVIDIA and Local AI Community Fuel Open Source Models and Intelligent Agentshttps://blogs.nvidia.com/blog/local-ai-open-source-models-agents-nemotron/: The open source ecosystem is making it easier for AI enthusiasts and developers to build, customize and run increasingly capable agents locally. Thro... - NVIDIA Nemotron 3.5 Lightning and NeMo Switchyard Deliver Faster, Smarter, More Efficient Agentic AIhttps://blogs.nvidia.com/blog/nemotron-lightning-switchyard-rtx-dgx/: As AI shifts from chatbots to autonomous agents, open models are serving market demands for full control over where AI runs and how it’s deployed and ... - Scaling Test-Time Compute in Search Modehttps://weaviate.io/blog/search-mode-effort: Introducing medium, high, and ultrahigh effort tiers to the Query Agent's Search Mode.... - Building and Validating a Quantitative Trading Strategy with OctoBot, Walk-Forward Backtesting, Parameter Optimization, and Interactive Analysishttps://www.marktechpost.com/2026/08/11/building-and-validating-a-quantitative-trading-strategy-with-octobot-walk-forward-backtesting-parameter-optimization-and-interactive-analysis/: In this tutorial, we build a complete quantitative backtesting workflow with OctoBot and OctoBot-Script while keeping the environment isolated from Co... - Introducing CARE-X: Towards Clinically Useful Radiology VLMs with Auxiliary Supervision, Reward-Aligned Learning, and Tool-Augmented Measurementhttps://www.microsoft.com/en-us/research/blog/introducing-care-x-towards-clinically-useful-radiology-vlms-with-auxiliary-supervision-reward-aligned-learning-and-tool-augmented-measurement/: Radiology AI is evolving beyond report generation. CARE-X explores a unified approach that combines flexible reasoning, calibrated predictions, and me... - An unreleased Anthropic model made progress on one of math’s biggest unsolved problemshttps://techcrunch.com/2026/08/11/an-unreleased-anthropic-model-made-progress-on-one-of-maths-biggest-unsolved-problems/: For more than 150 years, the Riemann hypothesis has stood as one of the major unsolved problems in mathematics. Anthropic hasn't solved it — but the c... - General Catalyst leads $1.1B round into 2-month-old River AIhttps://techcrunch.com/2026/08/11/general-catalyst-leads-1-1b-round-into-2-month-old-river-ai/: River AI, a startup founded by xAI co-founder Igor Babuschkin, has a fascinating vision for personal agents and secured $1.1 billion out of the gate.... - The Video Production Stack Now Fits on One Desk: LTX-2.5 Launches as NVIDIA-Accelerated Open Weights World Modelhttps://www.marktechpost.com/2026/08/11/the-video-production-stack-now-fits-on-one-desk-ltx-2-5-launches-as-nvidia-accelerated-open-weights-world-model/: LTX-2.5 brings frontier video generation to local NVIDIA hardware: 6.8-second clips, native multishot, day-one ComfyUI, open weights. The post The Vid... - Daybreak models are now available on AWShttps://openai.com/index/daybreak-models-are-now-available-on-aws: OpenAI and AWS are making Daybreak cybersecurity capabilities available through Amazon Bedrock to support enterprise security workflows.... 💼 New Opportunities - Pixel One: Senior Product Designerhttps://weworkremotely.com/remote-jobs/pixel-one-senior-product-designer - Pixel One: Senior Visual designerhttps://weworkremotely.com/remote-jobs/pixel-one-senior-visual-designer - Justworks: International Consultant, APAChttps://weworkremotely.com/remote-jobs/justworks-international-consultant-apac - Remote: Account Executive DACHhttps://weworkremotely.com/remote-jobs/remote-account-executive-dach-1 - Justworks: Account Executive - In-Territory SF/Bay Area, CAhttps://weworkremotely.com/remote-jobs/justworks-account-executive-in-territory-sf-bay-area-ca - Amplitude : Senior Enterprise Account Executivehttps://weworkremotely.com/remote-jobs/amplitude-senior-enterprise-account-executive - Remote: Account Executive DACHhttps://weworkremotely.com/remote-jobs/remote-account-executive-dach - Harney & Sons Fine Teas: Associate – ERP Operations SAP Business Onehttps://weworkremotely.com/remote-jobs/harney-sons-fine-teas-associate-erp-operations-sap-business-one - Toptal: Data Scientist for Top Cosmetic firmhttps://weworkremotely.com/remote-jobs/toptal-data-scientist-for-top-cosmetic-firm - Toptal: Tech Lead - Manufacturing Data & AI Platforms - Global Pharmaceutical Companyhttps://weworkremotely.com/remote-jobs/toptal-tech-lead-manufacturing-data-ai-platforms-global-pharmaceutical-company

View Daily Recap

News• Aug 12, 2026

M5B Daily Perspective (Technical Deep Dive): Navigating the Complex Landscape of Artificial Intelligence Engineering

The field of artificial intelligence has experienced unprecedented growth in recent years, with numerous breakthroughs and innovations transforming the way we live and work. As we delve into the technical architecture and engineering challenges of AI, it becomes evident that the development of these complex systems is a multifaceted and intricate process. From the design of large-scale AI inference workloads to the creation of dedicated desktop apps for Linux operating systems, the technical landscape of AI is constantly evolving. One of the primary concerns for AI engineers is the management of large-scale AI inference workloads, which can be a daunting task due to the sheer volume of data and computational resources required. The memory wall, a phenomenon where GPUs are underutilized due to inefficient memory allocation, is a significant obstacle that must be overcome. To address this challenge, researchers and developers are exploring novel strategies for managing AI workloads, including the use of smarter infrastructure design and optimized data pipelines. For instance, the Data Infrastructure Summit, a virtual conference that brought together experts from the likes of USBank and Bet, highlighted the importance of data infrastructure in supporting the growth of AI. As we navigate the complex landscape of AI engineering, it is essential to consider the role of open-source projects and community-driven initiatives. The Ultimate Guide to Contributing to Open Source Projects provides a comprehensive overview of the benefits and challenges of contributing to open-source projects, emphasizing the importance of collaboration and knowledge sharing in the AI community. Moreover, the development of new power architectures is crucial for scaling AI compute performance, as every new generation of accelerated computing demands more from the underlying infrastructure. The need for more efficient and scalable architectures is further underscored by the growing demand for AI applications, such as real-time clinical video consultation capabilities, which require significant computational resources and infrastructure support. The technical architecture of AI systems is also influenced by the choice of programming libraries and frameworks. The debate surrounding the use of Polars versus Pandas, two popular Python data libraries, highlights the importance of selecting the right tools for the task at hand. While Pandas is a well-established and widely-used library, Polars offers significant performance advantages, making it an attractive alternative for AI developers. Furthermore, the development of dedicated desktop apps, such as OpenAI's ChatGPT desktop app for Linux, demonstrates the growing need for user-friendly and accessible AI interfaces. The success of such apps is closely tied to the underlying technical architecture, which must be designed to support seamless and efficient user interactions. The intersection of AI and other fields, such as medicine and finance, is also giving rise to new and innovative applications. The development of AMIE, a research medical AI system, demonstrates the potential of AI in clinical settings, particularly in the context of real-time video consultations. Similarly, the use of AI in finance, such as in quantitative trading strategies, requires the development of sophisticated models and algorithms that can navigate complex market dynamics. The introduction of CARE-X, a clinically useful radiology VLM with auxiliary supervision, reward-aligned learning, and tool-augmented measurement, highlights the growing importance of AI in medical imaging and diagnostics. As we continue to push the boundaries of AI, it is essential to address the challenges and limitations of current systems. The departure of Brad Lightcap, OpenAI's longtime COO, underscores the importance of leadership and vision in driving the growth and development of AI companies. The need for effective budget allocation and resource management is also critical, as highlighted by the article on budget splits and shadow prices. Moreover, the introduction of AI Persona labels by Spotify and the watermarking of AI-generated content by Claude demonstrate the growing awareness of the need for transparency and accountability in AI applications. The job market for AI professionals is also experiencing significant growth, with positions such as Tech Lead for Manufacturing Data & AI Platforms and Data Scientist for Top Cosmetic firms highlighting the demand for skilled and experienced AI engineers. The development of new tools and technologies, such as the LTX-2.5, a NVIDIA-accelerated open weights world model, is also creating new opportunities for innovation and collaboration. The $1.1B investment in River AI by General Catalyst and the progress made by an unreleased Anthropic model on one of math's biggest unsolved problems demonstrate the significant resources being dedicated to AI research and development. In conclusion, the technical landscape of AI is complex and multifaceted, with numerous challenges and opportunities for innovation and growth. As we continue to navigate this landscape, it is essential to prioritize collaboration, knowledge sharing, and transparency, while also addressing the technical and engineering challenges that underlie the development of AI systems. By doing so, we can unlock the full potential of AI and create a future where these technologies can be harnessed to drive positive change and improvement in all aspects of our lives. The future of AI is exciting and uncertain, but one thing is clear: the technical architecture and engineering challenges of AI will continue to play a critical role in shaping the trajectory of this rapidly evolving field.

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Research• Aug 12, 2026

Aug 12, 2026Economic ResearchReviewing the evidence on worker retraining programs

#Anthropic#Claude#LLM

News• Aug 11, 2026

Accel closes oversubscribed $550M India fund within weeks, 19 months after its last

The U.S. VC firm still has more than 55% of its previous $650 million India fund available for deployment.

#News#AI#TechCrunch

News• Aug 11, 2026

OpenAI launches ChatGPT desktop app for Linux

OpenAI is finally bringing a dedicated ChatGPT desktop app to Linux operating systems.

#News#AI#TechCrunch

News• Aug 11, 2026

Google’s Gemini app surges to one billion users

Gemini is keeping pace with OpenAI’s ChatGPT, which hit 1 billion monthly active users back in June.

#News#AI#TechCrunch

News• Aug 11, 2026

Brad Lightcap, OpenAI’s longtime COO, is leaving to ‘start something new’

One of OpenAI's longest serving executives is headed out the door, although the longtime COO told staff that he was "excited to help you all advance the mission from a different vantage point."

#News#AI#TechCrunch

News• Aug 11, 2026

AMIE, our research medical AI system, demonstrates real-time clinical video consultation capabilities in a first-of-its-kind study.

AMIE promotional video

#Google#AI

Tutorials• Aug 11, 2026

Stop Calling the First Significant Day a Win

Checking an A/B test until it crosses p The post Stop Calling the First Significant Day a Win appeared first on Towards Data Science.

#Towards Data Science#Medium

Research• Aug 11, 2026

The memory wall: Why your GPUs are sitting idle

Proven strategies for managing large-scale AI inference workloads through smarter infrastructure design.

#AI Accelerator Institute#AI#Research

Research• Aug 11, 2026

Data Infrastructure Summit Virtual Summit | May 2026

Catch on every session from Data Infrastructure Summit, with sessions from the likes of USBank, Betterworks, Meta, ServiceNow and more.

#AI Accelerator Institute#AI#Research

Hardware• Aug 11, 2026

Why Scaling AI Compute Performance Requires a New Power Architecture

Every new generation of accelerated computing demands more from the infrastructure underneath it — more compute performance, higher rack density and more efficient, scalable power distribution. The bottleneck isn’t just wattage. It’s how power gets from the grid to the GPU. In traditional power del...

#NVIDIA#GPU#Enterprise

Tutorials• Aug 11, 2026

Should AI Developers Make the Switch from Polars to Pandas?

Not all Python data libraries are created equal! The post Should AI Developers Make the Switch from Polars to Pandas? appeared first on Towards Data Science.

#Towards Data Science#Medium

Tutorials• Aug 11, 2026

The Ultimate Guide to Contributing to Open Source Projects

This guide walks through what contributing to open source projects actually covers, how to pick a project that will actually respond to you, the exact git mechanics, and more.

#KDnuggets#Data Science#Learning

Engineering• Aug 11, 2026

Thinking of ACE? We Can Do It with Fewer Tokens

#HuggingFace#OpenSource#Models

Tutorials• Aug 11, 2026

The Budget Split That Explains Itself

How to diversify a budget without losing the shadow prices that explain the result The post The Budget Split That Explains Itself appeared first on Towards Data Science.

#Towards Data Science#Medium

News• Aug 11, 2026

Spotify will label ‘AI Persona’ profiles and exclude their music from recommendations

Spotify is introducing “AI Persona” labels for artist profiles that represent AI-generated identities and will exclude their music from editorial, algorithmic, and personalized recommendations by default.

#News#AI#TechCrunch

Tutorials• Aug 11, 2026

Claude Now Watermarks Everything It Makes

First, pick the line that applies to you. Since August 2nd, 2026, Claude marks all content during generation. For instance, text receives a hidden watermark, while files receive a signature. Anthropic committed to the EU AI Act’s Code of Practice on Transparency of AI-Generated Content. Consequently...

#Analytics Vidhya#Data Science

Research• Aug 11, 2026

Tracing sources of epistemic uncertainty in deep learning predictions: homo- and hetero-scedastic linearized estimators

arXiv:2608.07630v1 Announce Type: new Abstract: We adapt two classical statistical estimators for quantifying uncertainty to modern deep learning, in order to provide clearer insights into uncertainty attributable to two sources : aleatoric uncertainty, or locally scarce data. Our approach leverage...

#ArXiv#Machine Learning#Academic

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