David Jacobs

19 papers · 2017–2024 · 7 conferences · across top CS/AI conferences

Achievements

+10 more ↓

🐝 Cross-Pollinator (12) 🏃 Academic Marathon (7) 🌉 Interdisciplinary Bridge 🌍 Conference Polyglot (7) 🌈 Renaissance Researcher (6)

🌍 Conference Polyglot (7) 🏃 Academic Marathon (7) 🌈 Renaissance Researcher (6) 👑 Triple Crown 🧬 Topic Evolution ⚡ Prolific Year (5) 💎 Century Club (19) 🚀 Conference Pioneer 🗃️ Keyword Collector (87) 🔥 Unstoppable (8)

Conferences

NIPS (8) CVPR (3) ICCV (3) ICLR (2) AISTATS (1) ECCV (1) ICML (1)

Top co-authors

Songwei Ge (5) Tom Goldstein (5) Basri Ronen (4) Meirav Galun (3) Yoni Kasten (3) Abhay Yadav (3) Amnon Geifman (3) Shlok Mishra (3) Vasu Singla (3) Jonas Geiping (2)

Research topics

Machine Learning (1)

Keywords

contrastive learning (3) neural tangent kernel (3) representation learning (3) convolutional neural network (2) eigenvalue decay (2) unsupervised learning (2) adversarial training (2) zero-shot learning (1) non-convex optimization (1) stochastic gradient descent (1) model security (1) data poisoning (1) domain generalization (1) domain adaptation (1) in-context learning (1) few-shot learning (1) self-supervised learning (1) video captioning (1) video generation (1) scene understanding (1)

Papers

CALVIN: Improved Contextual Video Captioning via Instruction Tuning NIPS 2024 LD-ZNet: A Latent Diffusion Approach for Text-Based Image Segmentation ICCV 2023 Hyperbolic Contrastive Learning for Visual Representations Beyond Objects CVPR 2023 Towards Combinatorial Generalization for Catalysts: A Kohn-Sham Charge-Density Approach NIPS 2023 Preserve Your Own Correlation: A Noise Prior for Video Diffusion Models ICCV 2023 HaLP: Hallucinating Latent Positives for Skeleton-Based Self-Supervised Learning of Actions CVPR 2023 Long Video Generation with Time-Agnostic VQGAN and Time-Sensitive Transformer ECCV 2022 On the Spectral Bias of Convolutional Neural Tangent and Gaussian Process Kernels NIPS 2022 Autoregressive Perturbations for Data Poisoning NIPS 2022 Shift Invariance Can Reduce Adversarial Robustness NIPS 2021 Low Curvature Activations Reduce Overfitting in Adversarial Training ICCV 2021 Robust Contrastive Learning Using Negative Samples with Diminished Semantics NIPS 2021 Frequency Bias in Neural Networks for Input of Non-Uniform Density ICML 2020 On the Similarity between the Laplace and Neural Tangent Kernels NIPS 2020 SharinGAN: Combining Synthetic and Real Data for Unsupervised Geometry Estimation CVPR 2020 Adversarially robust transfer learning ICLR 2020 The Convergence Rate of Neural Networks for Learned Functions of Different Frequencies NIPS 2019 Stabilizing Adversarial Nets with Prediction Methods ICLR 2018 Automated Inference with Adaptive Batches AISTATS 2017