In this section, we shall show detailed derivations for the point-wise dependency estimation methods. Four approaches are discussed: Variational Bounds of Mutual Information, Density Matching, Probabilistic Classifier, and Density-Ratio Fitting. For convenience, we define Ω = X Y. We have PX,Y and PXPY (can also be written as PX PY) be the probability measures over σ algebras over Ω with their probability densities being the Radon-Nikodym derivatives (i.e., p(x,y) = dPX,Y/dµ and p(x)p(y) = dPXPY/dµwith µbeing the Lebesgue measure). These estimators have the logarithm of point-wise dependency (PMI) as the intermediate product, which we will show in the following. We denote Mbe any class of functions m: Ω R. Proposition 1 (INWJ and its neural estimation, restating Nguyen-Wainwright-Jordan bound [5, 18]).

Few-shot classification aims to recognize unlabeled samples from unseen classes given only few labeled samples.

Although the performance over time is the main performance that we want to benchmark, as pointed out by [3] the posterior matching is another metric to measure how good the inverse models are. Below we show the posterior matching score using Maximum Mean Discrepancy (MMD) as a measurement of how close the inferred posterior density is comparing with the ground truth (rejection sampled) distribution. Note that for a real-life problem (D4: meta-material) with higher dimensionality, the rejection sampling becomes intractable. The 3 MMD kernel used was 0.05, 0.2 and 0.9. The code is also available on the repository.

This paper introduces a novel optimization method for differential neural architecture search, based on the theory of prediction with expert advice. Its optimization criterion is well fitted for an architecture-selection, i.e., it minimizes the regret incurred by a sub-optimal selection of operations. Unlike previous search relaxations, that require hard pruning of architectures, our method is designed to dynamically wipe out inferior architectures and enhance superior ones. It achieves an optimal worst-case regret bound and suggests the use of multiple learning-rates, based on the amount of information carried by the backward gradients. Experiments show that our algorithm achieves a strong performance over several image classification datasets. Specifically, it obtains an error rate of 1.6% for CIFAR-10, 23.9% for ImageNet under mobile settings, and achieves state-of-the-art results on three additional datasets.

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

Autonomous agents situated in real-world environments must be able to master large repertoires of skills. While a single short skill can be learned quickly, it would be impractical to learn every task independently. Instead, the agent should share knowledge across behaviors such that each task can be learned efficiently, and such that the resulting model can generalize to new tasks, especially ones that are compositions or subsets of tasks seen previously. A policy conditioned on a goal or demonstration has the potential to share knowledge between tasks if it sees enough diversity of inputs. However, these methods may not generalize to a more complex task at test time. We introduce compositional plan vectors (CPVs) to enable a policy to perform compositions of tasks without additional supervision. CPVs represent trajectories as the sum of the subtasks within them. We show that CPVs can be learned within a one-shot imitation learning framework without any additional supervision or information about task hierarchy, and enable a demonstration-conditioned policy to generalize to tasks that sequence twice as many skills as the tasks seen during training. Analogously to embeddings such as word2vec in NLP, CPVs can also support simple arithmetic operations - for example, we can add the CPVs for two different tasks to command an agent to compose both tasks, without any additional training.

In this episode of “Uncanny Valley,” we get into how the Elon Musk-Sam Altman trial goes way beyond their rivalry and could have major implications both for OpenAI and also the AI industry at large.