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Measuring abstract reasoning in neural networks
A dataset and challenge designed to probe abstract reasoning, inspired by a well-known human IQ test, is proposed and ways to both measure and induce stronger abstract reasoning in neural networks are introduced.
Neural scene representation and rendering
The Generative Query Network (GQN) is introduced, a framework within which machines learn to represent scenes using only their own sensors, demonstrating representation learning without human labels or domain knowledge.
Insights on representational similarity in neural networks with canonical correlation
Comparing different neural network representations and determining how representations evolve over time remain challenging open questions in our understanding of the function of neural networks.
DD-PPO: Learning Near-Perfect PointGoal Navigators from 2.5 Billion Frames
It is shown that the scene understanding and navigation policies learned can be transferred to other navigation tasks -- the analog of "ImageNet pre-training + task-specific fine-tuning" for embodied AI.
On the importance of single directions for generalization
It is found that class selectivity is a poor predictor of task importance, suggesting not only that networks which generalize well minimize their dependence on individual units by reducing their selectivity, but also that individually selective units may not be necessary for strong network performance.
Human-level performance in 3D multiplayer games with population-based reinforcement learning
A tournament-style evaluation is used to demonstrate that an agent can achieve human-level performance in a three-dimensional multiplayer first-person video game, Quake III Arena in Capture the Flag mode, using only pixels and game points scored as input.
History-dependent variability in population dynamics during evidence accumulation in cortex
It is found that activity transitioned rapidly between different sets of active neurons in mice during a virtual navigation task, and evidence accumulation need not require the explicit competition between groups of neurons, but could instead emerge implicitly from general dynamical properties that instantiate short-term memory.
ConViT: Improving Vision Transformers with Soft Convolutional Inductive Biases
GPSA is introduced, a form of positional self-attention which can be equipped with a "soft" convolutional inductive bias and outperforms the DeiT on ImageNet, while offering a much improved sample efficiency.
One ticket to win them all: generalizing lottery ticket initializations across datasets and optimizers
It is found that, within the natural images domain, winning ticket initializations generalized across a variety of datasets, including Fashion MNIST, SVHN, CIFAR-10/100, ImageNet, and Places365, often achieving performance close to that of winning tickets generated on the same dataset.
Playing the lottery with rewards and multiple languages: lottery tickets in RL and NLP
Evaluating whether "winning ticket" initializations exist in NLP and reinforcement learning suggests that the lottery ticket hypothesis is not restricted to supervised learning of natural images, but rather represents a broader phenomenon in DNNs.