Hello, I recently got my paper rejected from a workshop (big womp :'( ) . Both reviewers said the paper sounds more like a technical report than a research paper. I followed the usual computer vision format for papers so I’m a bit confused by what that might actually mean. I would therefore like to hear the community’s opinion on what faux pas make a paper read as technical report. Thank you submitted by /u/obliviousphoenix2003 [link] [comments]

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New York, USA, May 15th, 2026/Chainwire/–E Estate Group Inc. announced that it will host E-Estate 1 Year Live: Washington DC Summit on June 13, 2026, bringing together company leadership, agents, buyers, strategic partners, and guests interested in the future of blockchain-based real estate ownership. The summit will take place at The Watergate Hotel in Washington, D.C. and will mark one year since the launch of the E-Estate platform. The event is designed as a milestone gathering for the […]

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Federated Learning (FL) enables collaborative model training across distributed clients without sharing raw data, thereby preserving privacy. However, FL often suffers from significant communication and computational overhead, limiting its scalability and sustainability. In this work, we introduce a Full Compression Pipeline (FCP) for FL in communication-constrained environments. FCP integrates three complementary deep compression techniques (pruning, quantization, and Huffman encoding) into a unified end-to-end framework. By compressing local models and communication payloads, FCP substantially reduces transmission costs and resource […]

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I’m writing a series called “Roads to a Universal World Model”. I think this is arguably the most consequential open problem in AI and robotics right now, and most coverage either hypes it as “the next LLM” or buries it in survey papers. I’m trying to do something different: trace each major path from origin to frontier, then look at where they converge and where they disagree. The approach is narrative-driven. I trace the people and decisions behind […]

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Adapting large pretrained models to new tasks efficiently and continually is crucial for real-world deployment but remains challenging due to catastrophic forgetting and the high cost of retraining. While parameter-efficient tuning methods like low rank adaptation (LoRA) reduce computational demands, they lack mechanisms for strict continual learning and knowledge integration, without relying on data replay, or multiple adapters. We propose Share, a novel approach to parameter efficient continual finetuning that learns and dynamically updates a single, shared low-rank […]

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Have you ever wondered how Amazon suggets to us items to buy when we’re looking at a product (labeled as “Frequently bought together”)? For example, when checking a GPU product (e.g. GTX 1080), amazon will tell you that the gpu, i7 cpu and RAM are frequently bought together. Which is true because a lot of people buy their components grouped when building a desktop pc.

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