Channel
@deeplearning_updates
On this record: Topic · Growth · Engagement · Posts · Cite this entry
132subscribers
-2 since we began measuring on 31 August 2026
Risers and fallers across the register · movement among entries of Under 1,000.
| Telegram ID | -1001361665973 |
|---|---|
| Type | Channel |
| Username | @deeplearning_updates |
| Created | Between 1 March 2018 and 31 July 2021 — estimated from Telegram’s id allocation, not measured. How this range is calculated. |
| First recorded | 31 August 2026 |
| Last confirmed live | 4 October 2026 |
| Measurements held | 4 |
| Confirmed unchanged | 1 time, most recently 4 October 2026 |
| On Telegram | t.me/deeplearning_updates |
Technology — a classification, not a measurement. An on-box language model (Qwen3.6-35B-A3B-FP8, prompt version 1) read this channel’s own recent posts on 4 October 2026 and assigned it the closest of 31 fixed categories, at 88% confidence. This is a model’s judgement about what the channel is likely to be about, not a fact this register measured the way a subscriber count or a view count is measured — it can be revised on a later pass, and it carries no weight anywhere else on this page. How this classification works, and why it has no browse page of its own yet.
| Measured (UTC) | Subscribers | Change |
|---|---|---|
| 4 Oct 2026, 00:59 | 132 | -1 |
| 10 Sept 2026, 15:20 | 133 | -1 |
| 31 Aug 2026, 12:43 | 134 | no change |
| 31 Aug 2026, 03:33 | 134 | first reading |
20 posts held, back to 14 December 2024 — the reader has not yet reached the start of this channel’s public history, so older posts may sit further back, unread. Read across 1 page of Telegram’s post history, 20 posts per page.
Nothing published in the last 30 days. ERR and ER are rolling 30-day measures, so there is nothing to compute — we hold 20 posts for this entry, the most recent from 18 December 2024. An engagement rate over an empty window would be a number about nothing.
arXiv digest 2024-12-15 🎛 Multimedia and Multimodal Learning - top 2 papers of the day: Multimodal Class-aware Semantic Enhancement Network for Audio-Visual Video Parsing Summary: We propose a multimodal class-aware semantic Enhancement network for audio-visual video parsing. Read on arXiv Provably Secure Robust Image Steganography via Cross-Modal Error Correction Summary: We propose a high-quality, provably secur…
arXiv digest 2024-12-15 🎵 Audio Processing - top 5 papers of the day: Efficient Whisper on Streaming Speech Summary: We introduce Whisper-T, an innovative framework combining both model and system-level optimizations for streaming speech. Read on arXiv Sonicmesh: Enhancing 3D Human Mesh Reconstruction in Vision-Impaired Environments With Acoustic Signals Summary: A novel approach combining acoustic signals with RG…
arXiv digest 2024-12-15 🖼️ Computer Vision - top 5 papers of the day: Reason-before-Retrieve: One-Stage Reflective Chain-of-Thoughts for Training-Free Zero-Shot Composed Image Retrieval Summary: We propose a novel training-free one-stage method, One-Stage Reflective Chain-of-Thought Reasoning for ZS-CIR (OSrCIR), which employs Multimodal Large Language Models to retain essential visual information in a single-stage …
arXiv digest 2024-12-15 📃 Computational Linguistics - top 5 papers of the day: Smaller Language Models Are Better Instruction Evolvers Summary: We propose Instruction Complex-Aware IFD (IC-IFD), which introduces instruction complexity in the original IFD score to evaluate the effectiveness of instruction data more accurately. 💻 Code: https://github.com/HypherX/Evolution-Analysis}{https://github.com/HypherX/Evolution…
arXiv digest 2024-12-14 🎛 Multimedia and Multimodal Learning - top 5 papers of the day: UCDR-Adapter: Exploring Adaptation of Pre-Trained Vision-Language Models for Universal Cross-Domain Retrieval Summary: We propose UCDR-Adapter, which enhances pre-trained models with adapters and dynamic prompt generation through a two-phase training strategy. 💻 Code: https://github.com/fine68/UCDR2024 Read on arXiv MambaPro: M…
arXiv digest 2024-12-14 🎵 Audio Processing - top 5 papers of the day: VinTAGe: Joint Video and Text Conditioning for Holistic Audio Generation Summary: We introduce VinTAGe, a flow-based transformer model that jointly considers text and video to guide audio generation. Read on arXiv Efficient Adaptation of Multilingual Models for Japanese ASR Summary: We fine-tuning Whisper-Tiny to improve performance in Japanese.…
arXiv digest 2024-12-14 🖼️ Computer Vision - top 5 papers of the day: Video Diffusion Transformers are In-Context Learners Summary: This paper investigates a solution for enabling in-context capabilities of video diffusion transformers, with minimal tuning required for activation. 💻 Code: https://github.com/feizc/Video-In-Context}. Read on arXiv SegACIL: Solving the Stability-Plasticity Dilemma in Class-Incrementa…
arXiv digest 2024-12-14 📃 Computational Linguistics - top 5 papers of the day: Towards Effective, Efficient and Unsupervised Social Event Detection in the Hyperbolic Space Summary: This work introduces an unsupervised framework, HyperSED, for social event detection in the hyperbolic space. 💻 Code: https://github.com/XiaoyanWork/HyperSED. Read on arXiv VisDoM: Multi-Document QA with Visually Rich Elements Using Mul…
arXiv digest 2024-12-13 🎛 Multimedia and Multimodal Learning - top 1 papers of the day: EVOS: Efficient Implicit Neural Training via EVOlutionary Selector Summary: We propose EVOS, an efficient training paradigm for accelerating Implicit Neural Representation (INR). Read on arXiv Disclaimer: paper selection is AI curated. by @deeplearning_updates
arXiv digest 2024-12-13 🎵 Audio Processing - top 5 papers of the day: CSL-L2M: Controllable Song-Level Lyric-to-Melody Generation Based on Conditional Transformer with Fine-Grained Lyric and Musical Controls Summary: We propose CSL-L2M, a controllable song-level lyric-to-melody generation method based on an in-attention Transformer decoder with fine-grained lyric and musical controls. 💻 Code: https://lichaiustc.gith…
arXiv digest 2024-12-13 🖼️ Computer Vision - top 5 papers of the day: TSGaussian: Semantic and Depth-Guided Target-Specific Gaussian Splatting from Sparse Views Summary: We introduce a novel framework that combines semantic constraints with depth priors to avoid geometry degradation in challenging novel view synthesis tasks. 💻 Code: https://github.com/leon2000-ai/TSGaussian. Read on arXiv DeepSeek-VL2: Mixture-of-…
arXiv digest 2024-12-13 📃 Computational Linguistics - top 5 papers of the day: DeepSeek-VL2: Mixture-of-Experts Vision-Language Models for Advanced Multimodal Understanding Summary: We present DeepSeek-VL2, an advanced series of large Mixture-of-Experts (MoE) Vision-Language Models that significantly improves upon its predecessor, DeepSeeking-VL1. 💻 Code: https://github.com/deepseek-ai/DeepSeek-VL2. Read on arXiv …
Showing the 12 most recent of 20 posts we hold for @deeplearning_updates. View and reaction counts are the latest single reading for each post, not a live figure, and a recent post is still accumulating both. A view count marked ≈ was rounded by Telegram before we ever saw it — t.me prints views in full below 1,000 and to three significant figures above, so ≈1,200,000 means somewhere between 1,150,000 and 1,249,999. Unmarked counts are exact. Text is reproduced from the public post preview and truncated for length.
A live page changes as we take new readings, so a citation should name the measurement it is based on, not just the URL. The line below cites the subscriber count as measured 4 October 2026 — this entry's latest reading, not the date you are reading this.
“DLU - Deep Learning Updates” (@deeplearning_updates), 132 subscribers as measured 4 October 2026. Telegram Register, tgregister.com/channel/deeplearning_updates.
Full measurement history, CC BY 4.0. Every reading this register holds for this entry, not just the latest one, as a dated, downloadable record: CSV · JSON. Free to use with attribution to tgregister.com. Each file carries its own generation timestamp, which is the figure to cite for exactly when the data was retrieved.