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.
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Question: Empirical risk minimization (ERM), with proper loss function and regularization, is the common practice of supervised classification. In this paper, we study training arbitrary (from linear to deep) binary classifier from only unlabeled (U) data by ERM. We prove that it is impossible to estimate the risk of an arbitrary binary classifier in an unbiased manner given a single set of U data, but it becomes possible given two sets of U data with different class priors. These two facts answer a fundamental question---what the minimal supervision is for training any binary classifier from only U data. Following these findings, we propose an ERM-based learning method from two sets of U data, and then prove it is consistent. Experiments demonstrate the proposed method could train deep models and outperform state-of-the-art methods for learning from two sets of U data.

Answer: Three class priors are all you need to train deep models from only U data, while any two should not be enough.

Question: 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

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

Question: The backpropagation algorithm is the most popular algorithm training neural networks nowadays. However, it suffers from the forward locking, backward locking and update locking problems, especially when a neural network is so large that its layers are distributed across multiple devices. Existing solutions either can only handle one locking problem or lead to severe accuracy loss or memory inefficiency. Moreover, none of them consider the straggler problem among devices. In this paper, we propose extbf{Layer-wise Staleness} and a novel efficient training algorithm, extbf{Diversely Stale Parameters} (DSP), which can address all these challenges without loss of accuracy nor memory issue. We also analyze the convergence of DSP with two popular gradient-based methods and prove that both of them are guaranteed to converge to critical points for non-convex problems. Finally, extensive experimental results on training deep convolutional neural networks demonstrate that our proposed DSP algorithm can achieve significant training speedup with stronger robustness and better generalization than compared methods.

Answer:
We propose Diversely Stale Parameters to break lockings of the backpropoagation algorithm and train a CNN in parallel.