开发者的 技术雷达

In Wu et. al, the MLE objective is computationally infeasible due to the high number of images in the dataset. Non-parametric Softmax Negative Log-Likelihood With large n, the denominator in (2) is hard to compute. Therefore, they use NCE (Noise-Contrastive Estimation). The NCE Objective Essentially, they approximate the difficult loss in (3) with the easier to compute loss in (7). However, we end up estimating the denominator anyways in (8). Why not just approximate the denominator in (2) with (8)? I asked Claude about this and it said something about it being a biased estimator, but I didn't really get that. I'm also a little confused on the connection of the original NCE formulation as being a way to estimate density and the way it is used here; do we do this because NCE loss is easier to compute and as m (the number of noise samples) increases, we get the gradients of NCE loss and gradients of NLL loss to match? submitted by /u/No_Balance_9777 [link] [comments]

Premise: this work is my first year PhD, and I dropped out for personal reasons. I still want to do research but independently. I have tried to submit my explainability paper to MICCAI. Sadly, for doubtful/good reasons, it got rejected. Among the reviewers, one explicitly suggested to make it stronger and that the work is "novel". I was wondering if a good strategy would be to work on it more (maybe improving also the time it takes for doing experiments, since currently it's a way too big model) and then submitting it to a journal, or first submitting to a workshop and then extend the research for a journal publication. Strategically wise, is it good to first workshop and then journal? MLCN/iMIMIC would be my choices. But I hear a lot about workshop being suboptimal. Given I am not currently optimising for a PhD, does it make sense to go for the long run and publish it as a journal paper/another conference? Thank you in advance. submitted by /u/KingPowa [link] [comments]

Hello, I'm trying to implement the Pocket TTS by kyutai-labs represented by this paper. Since they have didn't released the training/fine-tuning code. I'm trying to implement it on my own for learning some stuff. I have read the paper, tried to implement it with much more smaller parameters with smaller amount of data. I implemented this text to speech with one speaker on LJSpeech (1) and LibriSpeech clean subset but its hardly failing. For (1), Since it's a single speaker dataset I didn't added the voice cloning just simple text and target latents. flow matching loss became nearly 0.20 mse , EOS loss became very low like (x)e-(y) levels. But when infer with the model saved at 2800th epoch, It barily generating a meaningfull text even the text within its training set. Tried different techniques like Scheduled sampling for eliminate exposure bias (model was hallucinating sometimes and repeats same phrases twice), it didn't worked. Added std gaussian noise to ground truths, didn't worked. After struggling with lots of implementation I decided to move forward with quite larger dataset LibriSpeech because I thought that scale of the data was small. For (2), I read the paper again. No scheduled sampling, added the head multiplication etc, and implemented the paper in the librispeech dataset. I tried audio condition+ text tokens + BOS + target latents, and swapped the audio prompt with text tokens. I observed a tradeoff in this setup: if I put text tokens near to target latents, model generates better text but voice is not even close to audio prompt,and gibberish speak with better voice cloning when I put audio condition tokens near to target latents. And found out that loss is very spiky, and grad norm is exploding too you can see below the images. loss and lr values for setup 1 (LJSpeech) values for setup 2 (LibriSpeech) I used Pocket TTS' orijinal Mimi Audio Encoder by extracting it from Original model. What is your suggestions? Should I read paper over and over again? Should I increase the data amount by collecting from different sources(authors says that they used 88.000 hours of publicly available data)? Any system design problem? Trainings performed on RTX 5080 desktop gpu. I want to move on to bigger dataset but can't burn GPU credits for non-expected result. When should I increase dataset and start training on bigger clusters that could give me satisfyable results? submitted by /u/No-Motor-6274 [link] [comments]

Hi everyone, I’m a historical swordfighter (HEMA practitioner), and while I’m not a computer vision engineer or a roboticist, I’ve been reading a lot about the current bottlenecks in embodied AI, specifically around the Sim2Real gap and thin-object tracking. It occurred to me that high-level swordfighting is basically a perfect nightmare scenario for computer vision. We move at maximum athletic output, we shift our weight rapidly in non-linear ways (great for bipedal balance testing), we are completely covered in thick, bulky black jackets that hide our joints, and our steel blades move at 80mph, dropping below sub-pixel resolution or causing massive motion blur. I think it would be cool to have a computer vision scoring system for tournaments so I'm working to put together a mini-dataset using a synchronized multi-view setup (120/240fps) to map 100 hyper-trimmed clips of these specific physics edge cases. Since I'm non-technical, I used some AI assistance to help me structure what an AI-ready dataset card should look like, and I've hosted the placeholder page on Hugging Face to test the schema before I start shooting video with my clubmates. Here is the JSON line structure I'm currently planning to annotate each video with: { "clip_id": "hema_ls_001", "meta": { "weapon": "Longsword", "source_text": "Joachim Meyer (1570)", "capture_fps": 120 }, "time_stamps": { "start_frame": 120, "blade_contact_frame": 165, "recovery_end_frame": 210 }, "biomechanics": { "initial_guard": "Right Vom Tag", "ending_guard": "Left Ochs", "footwork_type": "Passing step offline", "strike_trajectory": "Diagonal Oberhau", "edge_alignment": "True edge" }, "computer_vision_hazards": { "occlusion_rating": "High (Crossed arms, bulky torso jacket)", "motion_blur_expected": true }, "frame_annotations": [ { "frame_index": 165, "is_contact_event": true, "keypoints_2d_pixel_coordinates": { "fencer_a_right_wrist": [412.5, 780.2], "fencer_a_left_wrist": [430.1, 795.4], "fencer_a_head_center": [425.0, 510.8], "fencer_b_right_wrist": [580.4, 765.1], "fencer_b_left_wrist": [565.0, 750.3], "sword_a_guard": [455.0, 810.0], "sword_a_tip": [890.4, 320.1], "sword_b_guard": [540.2, 790.6], "sword_b_tip": [310.5, 450.2] }, "segmentation_masks": { "sword_a_polygon_points": [[455.0, 810.0], [460.1, 805.2], [888.2, 322.5], [890.4, 320.1], [455.0, 810.0]], "occluded_pixels_detected": true } } ] } My questions for the researchers here: Does this metadata structure actually give you what you need to test trajectory prediction or pose estimation? Are there any specific keypoints (like explicit crossguard coordinates or footwork velocity metrics) that your models are starving for that I should add to the annotations while I'm doing the manual work? You can check out the full dataset description card and leave feedback or join the beta waitlist directly on Hugging Face here: https://huggingface.co/datasets/benito87/longsword-spatial-physics-100 I want to make sure this is actually useful, so any brutal feedback on the structure or parameters is highly appreciated. submitted by /u/fonssagrives [link] [comments]

I’m pretty annoyed. We’re a small AI startup building a real-time coding agent. Our p95 latency requirements are tight (and self imposed, but thats the product). We need sustained high-throughput inference with ~1-2k tokens/second. Been on the Cerebras waitlist for months trying to get API access. We’re not doing training so don’t need a warehouse of H100s. We need fast, high-throughput ASIC inference for a specific production workload. Cerebras’ just went public and they basically have no compute how is that possible? Well turns out OpenAI and Cerebras for OpenAI to buy like $20b worth of these chips. This has effectively pre-allocated the vast majority of Cerebras’ near-term inference capacity to a single customer. I mean, none of us can compete with that The result is that this deal situation has made their API waitlist functionally infinite for anyone who isn’t a hyperscaler. Legit making me pull my hair out. submitted by /u/Kortopi-98 [link] [comments]

SQLite 是一个 C 语言库，实现了一个小型、快速、独立、高可靠性、全功能的 SQL 数据库引擎。SQLite 是世界上使用最多的数据库引擎。SQLite 的源代码属于公共领域，每个人都可以免费使用，用于任何目的。 SQLite 3.53.3 现已发布，具体更新内容包括： Prior changes from version 3.53.0 修复 WAL 重置数据库损坏漏洞。 ...

CloudCanal 免费社区版 是 ClouGence 公司推出的一款全自研、可视化、自动化数据迁移同步工具，具备 结构迁移、数据迁移、数据同步、数据校验、数据订正 等功能，支持 60+ 款流行关系型数据库、实时数仓、消息中间件、缓存数据库和搜索引擎之间数据互通，其中包含国产数据库 OceanBase、PolarDB、TiDB、StarRocks、Doris...

一、引言 在现代运维和 DevOps 体系中，任务调度始终是不可或缺的一环。无论是批量执行服务器脚本、定时采集监控数据、还是编排复杂的 CI/CD 流水线，一个稳定、高性能、可扩展的调度系统都是基础底座。 Jiascheduler 正是这样一款用 Rust 编写、面向大规模分布式场景的开源任务调度器。它不仅能将用户脚本同时推送到数万...

CodeForge v26.3.0 是一次以调试、AI、数据与工作区为核心的大版本：把一套基于 DAP 的可视化调试器装进编辑器，覆盖 Python / Go / Rust / C / C++；扩展选中代码的 AI 解释·重构·测试与代码库上下文对话；带来结果导出、ER 图、交互式事务等数据库能力；引入多根工作区与大量编辑器生产力功能（.editorconfig、自动换...

今天 GitHub 上没有那种一骑绝尘的爆款，但有意思的是，前 8 名里挤进来 4 个新面孔。昨天还高居第一的 DeusData/codebase-memory-mcp 直接掉出前 50，第二名 xbtlin/ai-berkshire 也滑到第 13。更替来得比想象中快。 而今天故事的主角，是那个一天涨了 1425 星的 msitarzewski/agency-agents。 今日值得装上玩的 3 个 ...

Rust 前端打包工具 Rspack 2.1 现已正式发布。 Rspack （读音为 /'ɑrspæk/）是基于 Rust 语言开发的 Web 构建工具，拥有高性能、兼容 Webpack 生态、定制性强等多种优点。一些值得关注的变更如下： 性能提升 React Compiler Rust 版本 构建性能优化 TypeScript 7 支持 更快的循环依赖检查 新特性 支持 import.meta.gl...

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中国大陆、台湾、韩国、日本四家凑成了一条全球独一份的算力硬件产业链——拆开看，靠的是分工、积累、政策、成本和内需五件事。 https://2aran.com/articles/research/topics/east-asia-compute-hardware-cluster

端午节就刷到 deepseek v4.1 的消息，如何如何牛逼，让人很是期待，说是这个月底发布，怎么最近几天没消息了啊 还是说现在根本没有 deepseek v4.1 的计划，只是网络谣传啊

刚才 20260630 10 点 08 重置了，用了一会 看 10 点 15 又重置了 10 点 19 又重置了

这种方式速度咋样？ 有没有用过的兄弟讲讲