TASK DEFINITION: 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.
PROBLEM: In this work, we focus on the problem of grounding language by training an agent
 to follow a set of natural language instructions and navigate to a target object
 in a 2D grid environment. The agent receives visual information through raw
 pixels and a natural language instruction telling what task needs to be achieved.
 Other than these two sources of information, our model does not have any prior
 information of both the visual and textual modalities and is end-to-end trainable.
 We develop an attention mechanism for multi-modal fusion of visual and textual
 modalities that allows the agent to learn to complete the navigation tasks and also
 achieve language grounding. Our experimental results show that our attention
 mechanism outperforms the existing multi-modal fusion mechanisms proposed in
 order to solve the above mentioned navigation task. We demonstrate through the
 visualization of attention weights that our model learns to correlate attributes of
 the object referred in the instruction with visual representations and also show
 that the learnt textual representations are semantically meaningful as they follow
 vector arithmetic and are also consistent enough to induce translation between instructions
 in different natural languages. We also show that our model generalizes
 effectively to unseen scenarios and exhibit zero-shot generalization capabilities.
 In order to simulate the above described challenges, we introduce a new 2D environment
 for an agent to jointly learn visual and textual modalities

SOLUTION: Attention based architecture for language grounding via reinforcement learning in a new customizable 2D grid environment  

PROBLEM:   The goal of compressed sensing is to learn a structured signal $x$
  from a limited number of noisy linear measurements $y \approx Ax$.  In
  traditional compressed sensing, ``structure'' is represented by
  sparsity in some known basis.   Inspired by the success of deep
  learning in modeling images, recent work starting with~\cite{BDJP17}
  has instead considered structure to come from a generative model
  $G: \R^k \to \R^n$.  We present two results establishing the
  difficulty of this latter task, showing that existing bounds are
  tight.

   First, we provide a lower bound matching the~\cite{BDJP17} upper
  bound for compressed sensing from $L$-Lipschitz generative models
  $G$.  In particular, there exists such a function that requires
  roughly $\Omega(k \log L)$ linear measurements for sparse recovery
  to be possible.   This holds even for the more relaxed goal of
  \emph{nonuniform} recovery.

   Second, we show that generative models generalize sparsity as a
  representation of structure.   In particular, we construct a
  ReLU-based neural network $G: \R^{2k} \to \R^n$ with $O(1)$ layers
  and $O(kn)$ activations per layer, such that the range of $G$
  contains all $k$-sparse vectors.


SOLUTION: Lower bound for compressed sensing w/ generative models that matches known upper bounds

PROBLEM: Recurrent neural networks (RNNs) are particularly well-suited for modeling long-term dependencies in sequential data, but are notoriously hard to train because the error backpropagated in time either vanishes or explodes at an exponential rate. While a number of works attempt to mitigate this effect through gated recurrent units, skip-connections, parametric constraints and design choices, we propose a novel incremental RNN (iRNN), where hidden state vectors keep track of incremental changes, and as such approximate state-vector increments of Rosenblatt's (1962) continuous-time RNNs. iRNN exhibits identity gradients and is able to account for long-term dependencies (LTD). We show that our method is computationally efficient overcoming overheads of many existing methods that attempt to improve RNN training, while suffering no performance degradation. We demonstrate the utility of our approach with extensive experiments and show competitive performance against standard LSTMs on LTD and other non-LTD tasks.


SOLUTION:
Incremental-RNNs resolves exploding/vanishing gradient problem by updating state vectors based on difference between previous state and that predicted by an ODE.