Search Results for author:
Found 9 papers, 2 papers with code
Active Data Curation Effectively Distills Large-Scale Multimodal Models
Knowledge distillation (KD) is the de facto standard for compressing large-scale models into smaller ones.
Data curation via joint example selection further accelerates multimodal learning
Multimodal contrastive objectives expose the dependencies between data and thus naturally yield criteria for measuring the joint learnability of a batch.
Probing Biological and Artificial Neural Networks with Task-dependent Neural Manifolds
Recently, growth in our understanding of the computations performed in both biological and artificial neural networks has largely been driven by either low-level mechanistic studies or global normative approaches.
Layerwise complexity-matched learning yields an improved model of cortical area V2
Finally, when the two-stage model is used as a fixed front-end for a deep network trained to perform object recognition, the resultant model (LCL-V2Net) is significantly better than standard end-to-end self-supervised, supervised, and adversarially-trained models in terms of generalization to out-of-distribution tasks and alignment with human behavior.
Self-supervised video pretraining yields robust and more human-aligned visual representations
Humans learn powerful representations of objects and scenes by observing how they evolve over time.
Where Should I Spend My FLOPS? Efficiency Evaluations of Visual Pre-training Methods
Self-supervised methods have achieved remarkable success in transfer learning, often achieving the same or better accuracy than supervised pre-training.
Self-Supervised Learning of a Biologically-Inspired Visual Texture Model
These responses are processed by a second stage (analogous to cortical area V2) consisting of convolutional filters followed by half-wave rectification and pooling to generate V2 'complex cell' responses.
A Linear Systems Theory of Normalizing Flows
Normalizing Flows are a promising new class of algorithms for unsupervised learning based on maximum likelihood optimization with change of variables.
Neural Networks for Efficient Bayesian Decoding of Natural Images from Retinal Neurons
Decoding sensory stimuli from neural signals can be used to reveal how we sense our physical environment, and is valuable for the design of brain-machine interfaces.
