Search Results for author:
Found 12 papers, 8 papers with code
Amplifying human performance in combinatorial competitive programming
Recent years have seen a significant surge in complex AI systems for competitive programming, capable of performing at admirable levels against human competitors.
Transformers meet Neural Algorithmic Reasoners
Transformers have revolutionized machine learning with their simple yet effective architecture.
The CLRS-Text Algorithmic Reasoning Language Benchmark
Three years ago, a similar issue was identified and rectified in the field of neural algorithmic reasoning, with the advent of the CLRS benchmark.
Perception Test: A Diagnostic Benchmark for Multimodal Video Models
We propose a novel multimodal video benchmark - the Perception Test - to evaluate the perception and reasoning skills of pre-trained multimodal models (e. g. Flamingo, SeViLA, or GPT-4).
Ranked #1 on
Point Tracking
on Perception Test
Laser: Latent Set Representations for 3D Generative Modeling
NeRF provides unparalleled fidelity of novel view synthesis: rendering a 3D scene from an arbitrary viewpoint.
TAP-Vid: A Benchmark for Tracking Any Point in a Video
Generic motion understanding from video involves not only tracking objects, but also perceiving how their surfaces deform and move.
Perception Test: A Diagnostic Benchmark for Multimodal Models
We propose a novel multimodal benchmark – the Perception Test – that aims to extensively evaluate perception and reasoning skills of multimodal models.
A Generalized Lottery Ticket Hypothesis
We introduce a generalization to the lottery ticket hypothesis in which the notion of "sparsity" is relaxed by choosing an arbitrary basis in the space of parameters.
Knowledge distillation: A good teacher is patient and consistent
In particular, we uncover that there are certain implicit design choices, which may drastically affect the effectiveness of distillation.
Ranked #495 on
Image Classification
on ImageNet
Towards Understanding Normalization in Neural ODEs
Normalization is an important and vastly investigated technique in deep learning.
Interpolation Technique to Speed Up Gradients Propagation in Neural ODEs
We propose a simple interpolation-based method for the efficient approximation of gradients in neural ODE models.
MUSCO: Multi-Stage Compression of neural networks
The low-rank tensor approximation is very promising for the compression of deep neural networks.
