Modeling the effect of sequence variation on function is a fundamental problem for understanding and designing proteins.

Stabilizing an unknown control system is one of the most fundamental problems in control systems engineering. In this paper, we provide a simple, model-free algorithm for stabilizing fully observed dynamical systems. While model-free methods have become increasingly popular in practice due to their simplicity and flexibility, stabilization via direct policy search has received surprisingly little attention. Our algorithm proceeds by solving a series of discounted LQR problems, where the discount factor is gradually increased. We prove that this method efficiently recovers a stabilizing controller for linear systems, and for smooth, nonlinear systems within a neighborhood of their equilibria. Our approach overcomes a significant limitation of prior work, namely the need for a pre-given stabilizing control policy. We empirically evaluate the effectiveness of our approach on common control benchmarks.

Deep learning has enabled algorithms to generate realistic images. However, accurately predicting long video sequences requires understanding long-term dependencies and remains an open challenge.

From CNNs to attention mechanisms, encoding inductive biases into neural networks has been a fruitful source of improvement in machine learning.

The inequality trivially holds for α 0. 5, so we only focus on the case when α < 0. 5. If the nonconformity score's rank has no temporal dependence, we have Remarks The alternative update rule in Eq. 37 is not just used to prove Theorem 4.4. This is because Eq. 37 does not become more conservative on average Note that with Eq. 9, we have a T α + α + γ γT (47) By taking the limit of both sides, we are done. The task is to predict the (allowed) claim amount for the next visit. It has hourly temperature and electricity load data for one utility.

A canonical example in healthcare is predicting patient outcomes using physiological time-series data, where a population of patients composes a cross-section.

It is a Monday afternoon in August, and I am on the internet watching a former cable-news anchor interview a dead teenager on Substack. This dead teenager--Joaquin Oliver, killed in the mass shooting at Marjory Stoneman Douglas High School, in Parkland, Florida--has been reanimated by generative AI, his voice and dialogue modeled on snippets of his writing and home-video footage. The animations are stiff, the model's speaking cadence is too fast, and in two instances, when it is trying to convey excitement, its pitch rises rapidly, producing a digital shriek. How many people, I wonder, had to agree that this was a good idea to get us to this moment? I feel like I'm losing my mind watching it. Jim Acosta, the former CNN personality who's conducting the interview, appears fully bought-in to the premise, adding to the surreality: He's playing it straight, even though the interactions are so bizarre. Acosta asks simple questions about Oliver's interests and how the teenager died.