Neural Information Processing Systems http://nips.cc/

Neural Information Processing Systems http://nips.cc/

Linear Dynamical Systems (LDSs) are fundamental tools for modeling spatiotemporal data in various disciplines.

We propose a novel adaptive approximation approach for test-time resource-constrained prediction motivated by Mobile, IoT, health, security and other applications, where constraints in the form of computation, communication, latency and feature acquisition costs arise. We learn an adaptive low-cost system by training a gating and prediction model that limits utilization of a high-cost model to hard input instances and gates easy-to-handle input instances to a low-cost model. Our method is based on adaptively approximating the high-cost model in regions where low-cost models suffice for making highly accurate predictions. We pose an empirical loss minimization problem with cost constraints to jointly train gating and prediction models. On a number of benchmark datasets our method outperforms state-of-the-art achieving higher accuracy for the same cost.

Researchers from Australia's national science agency CSIRO, Federation University Australia and RMIT University have developed a method to improve the detection of audio deepfakes. The new technique, Rehearsal with Auxiliary-Informed Sampling (RAIS), is designed for audio deepfake detection -- a growing threat in cybercrime risks such as bypassing voice-based biometric authentication systems, impersonation and disinformation. It determines whether an audio clip is real or artificially generated (a'deepfake') and maintains performance over time as attack types evolve. In Italy earlier this year, an AI-cloned voice of its Defence Minister requested a €1M'ransom' from prominent business leaders, convincing some to pay. This is just one of many examples, highlighting the need for audio deepfake detectors.

Neural Information Processing Systems http://nips.cc/

Approximate probabilistic inference algorithms are central to many fields. Examples include sequential Monte Carlo inference in robotics, variational inference in machine learning, and Markov chain Monte Carlo inference in statistics.

Neural Information Processing Systems http://nips.cc/

High network communication cost for synchronizing gradients and parameters is the well-known bottleneck of distributed training.

We study minimax strategies for the online prediction problem with expert advice.