LTS4 @epfl_lts4
Signal Processing Laboratory (LTS4), Institute of Electrical Engineering, School of Engineering, École Polytechnique Fédérale de Lausanne lts4.epfl.ch Lausanne, Switzerland Joined November 2010-
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How can we inject new knowledge into LLMs without full retraining, forgetting, or breaking past edits? We introduce MEMOIR 📖— a scalable framework for lifelong model editing that reliably rewrites thousands of facts sequentially using a residual memory module. 🔥 🧵1/7
Fine-tuning pre-trained models leads to catastrophic forgetting, gains on one task cause losses on others. These issues worsen in multi-task merging scenarios. Enter LiNeS 📈, a method to solve them with ease. 🔥 🌐: lines-merging.github.io 📜: arxiv.org/abs/2410.17146 🧵 1/11
Struggling to integrate structural constraints in graph generative models? 🧩 Our new paper "Generative Modelling of Structurally Constrained Graphs" presents ConStruct to your rescue! 🚀 📜: arxiv.org/abs/2406.17341 🧵1/9
Wouldn't it be great if we could merge the knowledge of 20 specialist models into a single one without losing performance? 💪🏻 Introducing our new ICML paper "Localizing Task Information for Improved Model Merging and Compression". 🎉 📜: arxiv.org/pdf/2405.07813 🧵1/9
Having troubles with label noise in the training set? 🌧️ Our new ICML work "Pi-DUAL: Using privileged information to distinguish clean from noisy labels" provides an efficient solution! ☂️ 📜: arxiv.org/pdf/2310.06600 🧵1/8
Hello! I am happy to introduces CASTOR: a Score based method for uncovering causal temporal relationships in heterogeneous time series. This work is under the supervision of Prof. @pafrossard. Paper: arxiv.org/pdf/2311.01412…
Diffusion models struggling with large graphs? SparseDiff provides one solution: 💡 Sparse training for flexible GPU usage. ✔️ Integration with existing discrete diffusion models. 🚀 High performance on both small and large graphs. 📄 : arxiv.org/abs/2311.02142
Very happy to share that “Task Arithmetic in the Tangent Space” has been accepted as an oral presentation to @NeurIPSConf !! 🎉🎉 Thank you very much to the fantastic reviewers🙏 This is a great way to end a PhD 🤓
Very happy to share that “Task Arithmetic in the Tangent Space” has been accepted as an oral presentation to @NeurIPSConf !! 🎉🎉 Thank you very much to the fantastic reviewers🙏 This is a great way to end a PhD 🤓
Glad that Task Arithmetic in the Tangent Space will be an Oral at NeurIPS! 😁 Huge thanks to @gortizji, @pafrossard, and our reviewers! arxiv.org/abs/2305.12827
Glad that Task Arithmetic in the Tangent Space will be an Oral at NeurIPS! 😁 Huge thanks to @gortizji, @pafrossard, and our reviewers! arxiv.org/abs/2305.12827 https://t.co/Tw0lGfFRHs
Are you amazed by weight interpolation techniques to edit large pre-trained models? 🤔 Then you’re going to love our work 🚀: “Task Arithmetic in the Tangent Space”🏄 which explains why they work and gives a simple way to make them better! 🤓 📄: rb.gy/q9djm 🧵1/9
EPFL's School of Engineering is proud of Dr. Isabel Haasler's award for best SICON paper and her presentation at the #SIAMCT23 actu.epfl.ch/news/dr-isabel…
EPFL's School of Engineering is proud of Dr. Isabel Haasler's award for best SICON paper and her presentation at the #SIAMCT23 actu.epfl.ch/news/dr-isabel…
Don't miss our latest preprint: Fine-tuning in the tangent space enhances the editing of large pre-trained models like CLIP! 🔥 Check it out!
Don't miss our latest preprint: Fine-tuning in the tangent space enhances the editing of large pre-trained models like CLIP! 🔥 Check it out!
Bures-Wasserstein Means of Graphs. (arXiv:2305.19738v1 [stat.ML]) ift.tt/Vg0hbos
@Neuro_X_EPFL calls for applications to its new Postdoctoral Fellowships Program. Join us to do pioneering research, fully funded for up to 24 months. go.epfl.ch/Neuro_X_Postdo… @splacour40
Excited to share our paper in @Nature: We revisit the 50+ year-old maths problem with AI: how efficiently can we multiply two matrices? Surprisingly, the answer is still not known - even for 3x3 matrices! With AI, we discover many new efficient and exact algorithms. 1/13
Excited to share our paper in @Nature: We revisit the 50+ year-old maths problem with AI: how efficiently can we multiply two matrices? Surprisingly, the answer is still not known - even for 3x3 matrices! With AI, we discover many new efficient and exact algorithms. 1/13
Postdoc position opening in #AI for medicine jointly hosted by the labs of #EmmanuelAbbé and @pafrossard within the scope of a @CIS_EPFL supported research program. recruiting.epfl.ch/Vacancies/2529…
New *call for postdoctoral fellows* ai4science.epfl.ch for those who want to advance science with machine learning and combine their own ideas and projects with collaboration with laboratories @EPFL that excel in the development and applications of ML tools for sciences.
Deep learning phenomenon alert!🔬🚀 “Catastrophic overfitting is a bug but also a feature” 🐛 We find that single-step adversarial training fails due to the interaction between robust 💪 and non-robust 😈 features in the training set 🤯 📄: arxiv.org/abs/2206.08242 🧵 1/10
Tomorrow will be the first AI4Science Day at @SwissTech_EPFL. Come to attend talks, and posters, and meet the EPFL community working on ML applications to science! More info at go.epfl.ch/ai4science
Are you amazed by NeRFs, SIRENs or FFNs but don’t know why they work? 🤔 Then you’re going to love our work 🚀: “A structured dictionary perspective on implicit neural representations” @CVPR which explains how INRs manage to look so crisp! 🤓 📄: bit.ly/3adThjE 🧵 1/9

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