VisionLM
Collection
1884 items • Updated • 148
Abu Tyeb Azad
Mausul
·
AI & ML interests
NLP, Computer Vision, Multimodal Deep Learning
Recent Activity
upvoted a collection 1 day ago
VisionLM upvoted a collection 1 day ago
Multimodal LLM upvoted a collection 1 day ago
Deepseek PapersOrganizations
upvoted 3
collections 1 day ago
liked a
Space about 2 months ago
upvoted an article 2 months ago
Article
Finally, a Replacement for BERT: Introducing ModernBERT
- +13
bwarner, NohTow, bclavie, orionweller, ohallstrom, staghado, alexisgallagher, rbiswasfc, fladhak, tomaarsen, ncoop57, griffin, jph00, johnowhitaker, iacolippo
• • 740 liked a
dataset 2 months ago
upvoted an article 2 months ago
Article
🪆 Introduction to Matryoshka Embedding Models
- +1
tomaarsen, Xenova, osanseviero
• • 208 upvoted an article 5 months ago
Article
Gotchas in Tokenizer Behavior Every Developer Should Know
qgallouedec
• • 72 liked 2
Spaces 5 months ago
upvoted 3
articles 5 months ago
Article
Vision Language Models Explained
merve, edbeeching
• • 531 Article
Fine-tuning Florence-2 - Microsoft's Cutting-edge Vision Language Models
- +1
andito, merve, SkalskiP
• • 207 Article
SmolVLM - small yet mighty Vision Language Model
- +3
andito, merve, mfarre, eliebak, pcuenq
• • 417 liked a
Space 6 months ago
reacted to Kseniase's post with ❤️ 6 months ago
Post
6452
12 Types of JEPA
Since Yann LeCun together with Randall Balestriero released a new paper on JEPA (Joint-Embedding Predictive Architecture), laying out its theory and introducing an efficient practical version called LeJEPA, we figured you might need even more JEPA. Here are 7 recent JEPA variants plus 5 iconic ones:
1. LeJEPA → LeJEPA: Provable and Scalable Self-Supervised Learning Without the Heuristics (2511.08544)
Explains a full theory for JEPAs, defining the “ideal” JEPA embedding as an isotropic Gaussian, and proposes the SIGReg objective to push JEPA toward this ideal, resulting in practical LeJEPA
2. JEPA-T → JEPA-T: Joint-Embedding Predictive Architecture with Text Fusion for Image Generation (2510.00974)
A text-to-image model that tokenizes images and captions with a joint predictive Transformer, enhances fusion with cross-attention and text embeddings before training loss, and generates images by iteratively denoising visual tokens conditioned on text
3. Text-JEPA → Speaking in Words, Thinking in Logic: A Dual-Process Framework in QA Systems (2507.20491)
Converts natural language into first-order logic, with a Z3 solver handling reasoning, enabling efficient, explainable QA with far lower compute than large LLMs
4. N-JEPA (Noise-based JEPA) → Improving Joint Embedding Predictive Architecture with Diffusion Noise (2507.15216)
Connects self-supervised learning with diffusion-style noise by using noise-based masking and multi-level schedules, especially improving visual classification
5. SparseJEPA → SparseJEPA: Sparse Representation Learning of Joint Embedding Predictive Architectures (2504.16140)
Adds sparse representation learning to make embeddings more interpretable and efficient. It groups latent variables by shared semantic structure using a sparsity penalty while preserving accuracy
6. TS-JEPA (Time Series JEPA) → Joint Embeddings Go Temporal (2509.25449)
Adapts JEPA to time-series by learning latent self-supervised representations and predicting future latents for robustness to noise and confounders
Read further below ↓
It you like it, also subscribe to the Turing Post: https://www.turingpost.com/subscribe
Since Yann LeCun together with Randall Balestriero released a new paper on JEPA (Joint-Embedding Predictive Architecture), laying out its theory and introducing an efficient practical version called LeJEPA, we figured you might need even more JEPA. Here are 7 recent JEPA variants plus 5 iconic ones:
1. LeJEPA → LeJEPA: Provable and Scalable Self-Supervised Learning Without the Heuristics (2511.08544)
Explains a full theory for JEPAs, defining the “ideal” JEPA embedding as an isotropic Gaussian, and proposes the SIGReg objective to push JEPA toward this ideal, resulting in practical LeJEPA
2. JEPA-T → JEPA-T: Joint-Embedding Predictive Architecture with Text Fusion for Image Generation (2510.00974)
A text-to-image model that tokenizes images and captions with a joint predictive Transformer, enhances fusion with cross-attention and text embeddings before training loss, and generates images by iteratively denoising visual tokens conditioned on text
3. Text-JEPA → Speaking in Words, Thinking in Logic: A Dual-Process Framework in QA Systems (2507.20491)
Converts natural language into first-order logic, with a Z3 solver handling reasoning, enabling efficient, explainable QA with far lower compute than large LLMs
4. N-JEPA (Noise-based JEPA) → Improving Joint Embedding Predictive Architecture with Diffusion Noise (2507.15216)
Connects self-supervised learning with diffusion-style noise by using noise-based masking and multi-level schedules, especially improving visual classification
5. SparseJEPA → SparseJEPA: Sparse Representation Learning of Joint Embedding Predictive Architectures (2504.16140)
Adds sparse representation learning to make embeddings more interpretable and efficient. It groups latent variables by shared semantic structure using a sparsity penalty while preserving accuracy
6. TS-JEPA (Time Series JEPA) → Joint Embeddings Go Temporal (2509.25449)
Adapts JEPA to time-series by learning latent self-supervised representations and predicting future latents for robustness to noise and confounders
Read further below ↓
It you like it, also subscribe to the Turing Post: https://www.turingpost.com/subscribe
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liked a
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