instruction:
In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.
question:
We propose a solution for evaluation of mathematical expression. However, instead of designing a single end-to-end model we propose a Lego bricks style architecture. In this architecture instead of training a complex end-to-end neural network, many small networks can be trained independently each accomplishing one specific operation and acting a single lego brick. More difficult or complex task can then be solved using a combination of these smaller network. In this work we first identify 8 fundamental operations that are commonly used to solve arithmetic operations (such as 1 digit multiplication, addition, subtraction, sign calculator etc). These fundamental operations are then learned using simple feed forward neural networks. We then shows that different operations can be designed simply by reusing these smaller networks. As an example we reuse these smaller networks to develop larger and a more complex network to solve n-digit multiplication, n-digit division, and cross product. This bottom-up strategy not only introduces reusability, we also show that it allows to generalize for computations involving n-digits and we show results for up to 7 digit numbers. Unlike existing methods, our solution also generalizes for both positive as well as negative numbers.
answer:
We train many small networks each for a specific operation, these are then combined to perform complex operations

question:
Deterministic models are approximations of reality that are often easier to build and interpret than stochastic alternatives.
Unfortunately, as nature is capricious, observational data can never be fully explained by deterministic models in practice.
Observation and process noise need to be added to adapt deterministic models to behave stochastically, such that they are capable of explaining and extrapolating from noisy data.
Adding process noise to deterministic simulators can induce a failure in the simulator resulting in no return value for certain inputs -- a property we describe as ''brittle.''
We investigate and address the wasted computation that arises from these failures, and the effect of such failures on downstream inference tasks.
We show that performing inference in this space can be viewed as rejection sampling, and train a conditional normalizing flow as a proposal over noise values such that there is a low probability that the simulator crashes, increasing computational efficiency and inference fidelity for a fixed sample budget when used as the proposal in an approximate inference algorithm.
answer:
We learn a conditional autoregressive flow to propose perturbations that don't induce simulator failure, improving inference performance.

question:
Domain adaptation is an open problem in deep reinforcement learning (RL). Often, agents are asked to perform in environments where data is difficult to obtain. In such settings, agents are trained in similar environments, such as simulators, and are then transferred to the original environment. The gap between visual observations of the source and target environments often causes the agent to fail in the target environment. We present a new RL agent, SADALA (Soft Attention DisentAngled representation Learning Agent). SADALA first learns a compressed state representation. It then jointly learns to ignore distracting features and solve the task presented. SADALA's separation of important and unimportant visual features leads to robust domain transfer. SADALA outperforms both prior disentangled-representation based RL and domain randomization approaches across RL environments (Visual Cartpole and DeepMind Lab).
answer:
We present an agent that uses a beta-vae to extract visual features and an attention mechanism to ignore irrelevant features from visual observations to enable robust transfer between visual domains.