266 results on '"Tomaso A. Poggio."'
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2. Formation of Representations in Neural Networks.
3. How Neural Networks Learn the Support is an Implicit Regularization Effect of SGD.
4. Feature learning in deep classifiers through Intermediate Neural Collapse.
5. System Identification of Neural Systems: If We Got It Right, Would We Know?
6. Achieving Adversarial Robustness in Deep Learning-Based Overhead Imaging.
7. Norm-based Generalization Bounds for Sparse Neural Networks.
8. Representation Learning in Sensory Cortex: A Theory.
9. Norm-based Generalization Bounds for Compositionally Sparse Neural Networks.
10. How to guess a gradient.
11. Neural-Guided, Bidirectional Program Search for Abstraction and Reasoning.
12. Approximate Inference with Wasserstein Gradient Flows.
13. Deep Learning for Seismic Inverse Problems: Toward the Acceleration of Geophysical Analysis Workflows.
14. Fisher-Rao Metric, Geometry, and Complexity of Neural Networks.
15. Fast and Flexible Inference of Joint Distributions from their Marginals.
16. An analysis of training and generalization errors in shallow and deep networks.
17. SGD Noise and Implicit Low-Rank Bias in Deep Neural Networks.
18. Iterative regularization in classification via hinge loss diagonal descent.
19. Biologically Inspired Mechanisms for Adversarial Robustness.
20. Cross-Domain Adversarial Reprogramming of a Recurrent Neural Network.
21. Symmetry-adapted representation learning.
22. Explicit regularization and implicit bias in deep network classifiers trained with the square loss.
23. Distribution of Classification Margins: Are All Data Equal?
24. The Effects of Image Distribution and Task on Adversarial Robustness.
25. Do Deep Neural Networks Suffer from Crowding?
26. Nested Invariance Pooling and RBM Hashing for Image Instance Retrieval.
27. Compression of Deep Neural Networks for Image Instance Retrieval.
28. When and Why Are Deep Networks Better Than Shallow Ones?
29. Biologically-Plausible Learning Algorithms Can Scale to Large Datasets.
30. Hierarchically Local Tasks and Deep Convolutional Networks.
31. CUDA-Optimized real-time rendering of a Foveated Visual System.
32. For interpolating kernel machines, the minimum norm ERM solution is the most stable.
33. How Important Is Weight Symmetry in Backpropagation?
34. Holographic Embeddings of Knowledge Graphs.
35. Why and when can deep-but not shallow-networks avoid the curse of dimensionality: A review.
36. Double descent in the condition number.
37. Theory III: Dynamics and Generalization in Deep Networks.
38. Theoretical Issues in Deep Networks: Approximation, Optimization and Generalization.
39. Function approximation by deep networks.
40. Learning with a Wasserstein Loss.
41. Learning with Group Invariant Features: A Kernel Perspective.
42. Discriminative template learning in group-convolutional networks for invariant speech representations.
43. Convex Learning of Multiple Tasks and their Structure.
44. Representation Learning from Orbit Sets for One-Shot Classification.
45. Group Invariant Deep Representations for Image Instance Retrieval.
46. Eccentricity Dependent Deep Neural Networks: Modeling Invariance in Human Vision.
47. Is the Human Visual System Invariant to Translation and Scale?
48. Turing++ Questions: A Test for the Science of (Human) Intelligence.
49. Unsupervised learning of invariant representations.
50. Word-level invariant representations from acoustic waveforms.
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