Customer-obsessed science

Motion Compensated Temporal Filtering (MCTF) is a pre-processing approach employed prior to video encoding, for improving the compression efficiency. Prior MCTF designs (e.g. [1]) use pre-defined frame-level quantization parameters (QPs) for different slice types and temporal layers, and operate with a fixed Group of Pictures (GOP) structure. However, commercial encoders can adapt GOP structure based upon

We propose an approach to estimate the number of samples required for a model to reach a target performance. We find that the power law, the de facto principle to estimate model performance, leads to large error when using a small dataset (e.g., 5 samples per class) for extrapolation. This is because the log-performance error against the log-dataset size follows a nonlinear progression in the few-shot regime

Machine learning (ML)-powered football analytics has received considerable interest in recent years, with majority of existing analytic measures centered around offense strategies and performances. In contrast, the defensive side of the game has received relatively less attention and development. At the core of understanding and analyzing any defensive strategy is the coverage scheme, i.e., the rules and

Humans naturally decompose their environment into entities at the appropriate level of abstraction to act in the world. Allowing machine learning algorithms to derive this decomposition in an unsupervised way has become an important line of research. However, current methods are restricted to simulated data or require additional information in the form of motion or depth in order to successfully discover

We present HumanEvalX and MBXP, execution-based code completion benchmarks in 10+ programming languages. These datasets are generated by our conversion framework that transpiles prompts and test cases from original datasets (HumanEval and MBPP) to the corresponding data in a target language. Based on these benchmarks, we are able to evaluate code generation models in a multilingual fashion, and in particular