ali5341/scitldr-chat-format huggingface dataset:
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Meta-Teaching: Curriculum Generation for Lifelong Learning

Paper content:
Meta-learning will be crucial to creating lifelong, generalizable AI.
In practice, however, it is hard to define the meta-training task distribution that is used to train meta-learners.
If made too small, tasks are too similar for a model to meaningfully generalize.
If made too large, generalization becomes incredibly difficult.
We argue that both problems can be alleviated by introducing a teacher model that controls the sequence of tasks that a meta-learner is trained on.
This teacher model is incentivized to start the student meta-learner on simple tasks then adaptively increase task difficulty in response to student progress.
While this approach has been previously studied in curriculum generation, our main contribution is in extending it to meta-learning.
Humans are incredibly good at generalizing to unseen tasks.
But, humans are only able to do so because they lean on a vast history of experience.
Within a single lifespan, we begin by learning simple tasks: crawling, walking, talking.
As we age, we learn progressively more and more difficult tasks, borrowing from the simpler to inform the more complex.In order for machines to exhibit this same behavior, they have to learn how to generalize and borrow from previous experiences.
Once they can do so reliably, we move a step closer to the holy grail of Artificial General Intelligence.
However, artificial intelligence systems are incredibly brittle.
Because we have an incomplete understanding of how to best learn from past experiences, it is unclear how we can create robust, generalizable AI agents.We propose to tackle this problem by combining curriculum learning and meta learning into an approach called metateaching.
We aim to teach meta-AI agents to start from easy Preliminary work.
Under review by the International Conference on Machine Learning (ICML).
Do not distribute.
Sample meta-batch of tasks from perturbed task- DISPLAYFORM0 Evaluate L S T i with respect to K samples from task 5:Compute adapted student parameters using either a gradient based or gradient free update 6: end for DISPLAYFORM1 Approximate difficulty gradient of current task-space DISPLAYFORM2 Update adapted task-space parameters with gradient descent over i student losses: DISPLAYFORM3 .
9: end while tasks, progressively learn harder tasks, and use information about easy tasks to inform the harder ones.
In order to do so, we introduce a teacher that updates the difficulty of the current task in response to student progress.Before describing meta-teaching, we will discuss related works and provide preliminary information.

assistant: text: Teacher that trains meta-learners like humans
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: RANDOM MASK: Towards Robust Convolutional Neural Networks

Paper content:
Robustness of neural networks has recently been highlighted by the adversarial examples, i.e., inputs added with well-designed  perturbations which are imperceptible to humans but can cause the network to give incorrect outputs.
In this paper, we design a new CNN architecture that by itself has good robustness.
We introduce a simple but powerful technique, Random Mask, to modify existing CNN structures.
We show that CNN with Random Mask achieves state-of-the-art performance against black-box adversarial attacks without applying any adversarial training.
We next investigate the adversarial examples which “fool” a CNN with Random Mask.
Surprisingly, we find that these adversarial examples often “fool” humans as well.
This raises fundamental questions on how to define adversarial examples and robustness properly.

assistant: text: This paper proposes a simple technique for improving the robustness of neural networks against black-box attacks.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Scale-Equivariant Neural Networks with Decomposed Convolutional Filters

Paper content:
Encoding the input scale information explicitly into the representation learned by a convolutional neural network (CNN) is beneficial for many vision tasks especially when dealing with multiscale input signals.
We study, in this paper, a scale-equivariant CNN architecture with joint convolutions across the space and the scaling group, which is shown to be both sufficient and necessary to achieve scale-equivariant representations.
To reduce the model complexity and computational burden, we decompose the convolutional filters under two pre-fixed separable bases and truncate the expansion to low-frequency components.
A further benefit of the truncated filter expansion is the improved deformation robustness of the equivariant representation.
Numerical experiments demonstrate that the proposed scale-equivariant neural network with decomposed convolutional filters (ScDCFNet) achieves significantly improved performance in multiscale image classification and better interpretability than regular CNNs at a reduced model size.

assistant: text: We construct scale-equivariant convolutional neural networks in the most general form with both computational efficiency and proved deformation robustness.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Explaining Adversarial Examples with Knowledge Representation

Paper content:
Adversarial examples are modified samples that preserve original image structures but deviate classifiers.
Researchers have put efforts into developing methods for generating adversarial examples and finding out origins.
Past research put much attention on decision boundary changes caused by these methods.
This paper, in contrast, discusses the origin of adversarial examples from a more underlying knowledge representation point of view.
Human beings can learn and classify prototypes as well as transformations of objects.
While neural networks store learned knowledge in a more hybrid way of combining all prototypes and transformations as a whole distribution.
Hybrid storage may lead to lower distances between different classes so that small modifications can mislead the classifier.
A one-step distribution imitation method is designed to imitate distribution of the nearest different class neighbor.
Experiments show that simply by imitating distributions from a training set without any knowledge of the classifier can still lead to obvious impacts on classification results from deep networks.
It also implies that adversarial examples can be in more forms than small perturbations.
Potential ways of alleviating adversarial examples are discussed from the representation point of view.
The first path is to change the encoding of data sent to the training step.
Training data that are more prototypical can help seize more robust and accurate structural knowledge.
The second path requires constructing learning frameworks with improved representations.

assistant: text: Hybird storage and representation of learned knowledge may be a reason for adversarial examples.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Distributed Fine-tuning of Language Models on Private Data

Paper content:
One of the big challenges in machine learning applications is that training data can be different from the real-world data faced by the algorithm.
In language modeling, users’ language (e.g. in private messaging) could change in a year and be completely different from what we observe in publicly available data.
At the same time, public data can be used for obtaining general knowledge (i.e. general model of English).
We study approaches to distributed fine-tuning of a general model on user private data with the additional requirements of maintaining the quality on the general data and minimization of communication costs.
We propose a novel technique that significantly improves prediction quality on users’ language compared to a general model and outperforms gradient compression methods in terms of communication efficiency.
The proposed procedure is fast and leads to an almost 70% perplexity reduction and 8.7 percentage point improvement in keystroke saving rate on informal English texts.
Finally, we propose an experimental framework for evaluating differential privacy of distributed training of language models and show that our approach has good privacy guarantees.

assistant: text: We propose a method of distributed fine-tuning of language models on user devices without collection of private data
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Program Guided Agent

Paper content:
Developing agents that can learn to follow natural language instructions has been an emerging research direction.
While being accessible and flexible, natural language instructions can sometimes be ambiguous even to humans.
To address this, we propose to utilize programs, structured in a formal language, as a precise and expressive way to specify tasks.
We then devise a modular framework that learns to perform a task specified by a program – as different circumstances give rise to diverse ways to accomplish the task, our framework can perceive which circumstance it is currently under, and instruct a multitask policy accordingly to fulfill each subtask of the overall task.
Experimental results on a 2D Minecraft environment not only demonstrate that the proposed framework learns to reliably accomplish program instructions and achieves zero-shot generalization to more complex instructions but also verify the efficiency of the proposed modulation mechanism for learning the multitask policy.
We also conduct an analysis comparing various models which learn from programs and natural language instructions in an end-to-end fashion.

assistant: text: This paper investigates training RL agents with instructions and task decompositions formalized as programs, proposing a model for a program guided agent that interprets a program and proposes subgoals to an action module.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Why do These Match? Explaining the Behavior of Image Similarity Models

Paper content:
Explaining a deep learning model can help users understand its behavior and allow researchers to discern its shortcomings.
Recent work has primarily focused on explaining models for tasks like image classification or visual question answering.
In this paper, we introduce an explanation approach for image similarity models, where a model's output is a score measuring the similarity of two inputs rather than a classification.
In this task, an explanation depends on both of the input images, so standard methods do not apply.
We propose an explanation method that pairs a saliency map identifying important image regions with an attribute that best explains the match.
We find that our explanations provide additional information not typically captured by saliency maps alone, and can also improve performance on the classic task of attribute recognition.
Our approach's ability to generalize is demonstrated on two datasets from diverse domains, Polyvore Outfits and Animals with Attributes 2.

assistant: text: The paper proposes an explanation mechanism that pairs the typical saliency map regions together with attributes for similarity matching deep neural networks.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks

Paper content:
Neural network pruning techniques can reduce the parameter counts of trained networks by over 90%, decreasing storage requirements and improving computational performance of inference without compromising accuracy.
However, contemporary experience is that the sparse architectures produced by pruning are difficult to train from the start, which would similarly improve training performance.
We find that a standard pruning technique naturally uncovers subnetworks whose initializations made them capable of training effectively.
Based on these results, we articulate the "lottery ticket hypothesis:" dense, randomly-initialized, feed-forward networks contain subnetworks ("winning tickets") that - when trained in isolation - reach test accuracy comparable to the original network in a similar number of iterations.
The winning tickets we find have won the initialization lottery: their connections have initial weights that make training particularly effective.
We present an algorithm to identify winning tickets and a series of experiments that support the lottery ticket hypothesis and the importance of these fortuitous initializations.
We consistently find winning tickets that are less than 10-20% of the size of several fully-connected and convolutional feed-forward architectures for MNIST and CIFAR10.
Above this size, the winning tickets that we find learn faster than the original network and reach higher test accuracy.

assistant: text: Feedforward neural networks that can have weights pruned after training could have had the same weights pruned before training
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: MIDAS: Finding the Right Web Sources to Fill Knowledge Gaps

Paper content:
Knowledge bases, massive collections of facts (RDF triples) on diverse topics, support vital modern applications.
However, existing knowledge bases contain very little data compared to the wealth of information on the Web.
This is because the industry standard in knowledge base creation and augmentation suffers from a serious bottleneck: they rely on domain experts to identify appropriate web sources to extract data from.
Efforts to fully automate knowledge extraction have failed to improve this standard: these automated systems are able to retrieve much more data and from a broader range of sources, but they suffer from very low precision and recall.
As a result, these large-scale extractions remain unexploited.
In this paper, we present MIDAS, a system that harnesses the results of automated knowledge extraction pipelines to repair the bottleneck in industrial knowledge creation and augmentation processes.
MIDAS automates the suggestion of good-quality web sources and describes what to extract with respect to augmenting an existing knowledge base.
We make three major contributions.
First, we introduce a novel concept, web source slices, to describe the contents of a web source.
Second, we define a profit function to quantify the value of a web source slice with respect to augmenting an existing knowledge base.
Third, we develop effective and highly-scalable algorithms to derive high-profit web source slices.
We demonstrate that MIDAS produces high-profit results and outperforms the baselines significantly on both real-word and synthetic datasets.

assistant: text: This paper focuses on identifying high quality web sources for industrial knowledge base augmentation pipeline.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Efficient Receptive Field Learning by Dynamic Gaussian Structure

Paper content:
The visual world is vast and varied, but its variations divide into structured and unstructured factors.
Structured factors, such as scale and orientation, admit clear theories and efficient representation design.
Unstructured factors, such as what it is that makes a cat look like a cat, are too complicated to model analytically, and so require free-form representation learning.
We compose structured Gaussian filters and free-form filters, optimized end-to-end, to factorize the representation for efficient yet general learning.
Our experiments on dynamic structure, in which the structured filters vary with the input, equal the accuracy of dynamic inference with more degrees of freedom while improving efficiency.
(Please see https://arxiv.org/abs/1904.11487 for the full edition.)
Although the visual world is varied, there is nevertheless ubiquitous structure.
Free-form learned representations are structure-agnostic, making them general, but their not harnessing structure is computationally and statistically inefficient.
Structured representations like steerable filtering BID5 BID6 , scattering BID0 , and steerable networks BID1 efficiently express certain structures, but are constrained.
We propose the semi-structured composition of Gaussian and free-form filters to blur the line between free-form and structured representations.The effectiveness of strongly structured representations hinges on whether they encompass the true structure of the data.
If not, the representation is limiting, and subject to error.
At least, such is the case when structure substitutes for learning.
In this work we compose structured and free-form filters and learn both end-to-end ( FIG1 ).
The free-form parameters are not constrained by our composition for generality.
The structured parameters are low-dimensional for efficiency.We choose Gaussian structure to represent the spatial structures of scale, aspect, and orientation through covariance BID7 .
Optimizing these structured covariance parameters carries out a form of differentiable architecture search over receptive fields.
Since this structure is lowdimensional, it is computationally efficient and could be learned from limited data.

assistant: text: This paper proposes a structured convolution operator to model deformations of local regions of an image, which significantly reduced the number of parameters.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Hierarchical Summary-to-Article Generation

Paper content:
In this paper, we explore \textit{summary-to-article generation}: the task of generating long articles given a short summary, which provides finer-grained content control for the generated text.
To prevent sequence-to-sequence (seq2seq) models from degenerating into language models and better controlling the long text to be generated, we propose a hierarchical generation approach which first generates a sketch of intermediate length based on the summary and then completes the article by enriching the generated sketch.
To mitigate the discrepancy between the ``oracle'' sketch used during training and the noisy sketch generated during inference, we propose an end-to-end joint training framework based on multi-agent reinforcement learning.
For evaluation, we use text summarization corpora by reversing their inputs and outputs, and introduce a novel evaluation method that employs a summarization system to summarize the generated article and test its match with the original input summary.
Experiments show that our proposed hierarchical generation approach can generate a coherent and relevant article based on the given summary, yielding significant improvements upon conventional seq2seq models.

assistant: text: we explore the task of summary-to-article generation and propose a hierarchical generation scheme together with a jointly end-to-end reinforcement learning framework to train the hierarchical model.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Understanding Attention Mechanisms

Paper content:
Attention mechanisms have advanced the state of the art in several machine learning tasks.
Despite significant empirical gains, there is a lack of theoretical analyses on understanding their effectiveness.
In this paper, we address this problem by studying the landscape of population and empirical loss functions of attention-based neural networks.
Our results show that, under mild assumptions, every local minimum of a two-layer global attention model has low prediction error, and attention models require lower sample complexity than models not employing attention.
We then extend our analyses to the popular self-attention model, proving that they deliver consistent predictions with a more expressive class of functions.
Additionally, our theoretical results provide several guidelines for designing attention mechanisms.
Our findings are validated with satisfactory experimental results on MNIST and IMDB reviews dataset.

assistant: text: This paper proves from the theoretical perspective that attention networks can generalize better than non-attention baselines for fixed-attention (single-layer and multi-layer) and self-attention in the single layer setting.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Deep contextualized word representations

Paper content:
We introduce a new type of deep contextualized word representation that models both (1) complex characteristics of word use (e.g., syntax and semantics), and (2) how these uses vary across linguistic contexts (i.e., to model polysemy).
Our word vectors are learned functions of the internal states of a deep bidirectional language model (biLM), which is pretrained on a large text corpus.
We show that these representations can be easily added to existing models and significantly improve the state of the art across six challenging NLP problems, including question answering, textual entailment and sentiment analysis.
We also present an analysis showing that exposing the deep internals of the pretrained network is crucial, allowing downstream models to mix different types of semi-supervision signals.
We have introduced a general approach for learning high-quality deep context-dependent representations from biLMs, and shown large improvements when applying ELMo to a broad range of NLP tasks.
Through ablations and other controlled experiments, we have also confirmed that the biLM layers efficiently encode different types of syntactic and semantic information about wordsin-context, and that using all layers improves overall task performance.Our approach raises several interesting questions for future work, broadly organized into two themes."What
is the best training regime for learning generally useful NLP representations?" By choosing
a biLM training objective, we benefit from nearly limitless unlabeled text and can immediately apply advances in language modeling, an active area of current research. However, it
's possible that further decreases in LM perplexity will not translate to more transferable representations, and that other objective functions might be more suitable for learning general purpose representations."What is the
best way to use deep contextual representations for other tasks?" Our method of
using a weighted average of all layers from the biLM is simple and empirically successful. However, a deeper
fusion of the biLM layers with a target NLP architecture may lead to further improvements.

assistant: text: We introduce a new type of deep contextualized word representation that significantly improves the state of the art for a range of challenging NLP tasks.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Improving Sample Complexity with Observational Supervision

Paper content:
Supervised machine learning models for high-value computer vision applications such as medical image classification often require large datasets labeled by domain experts, which are slow to collect, expensive to maintain, and static with respect to changes in the data distribution.
In this context, we assess the utility of observational supervision, where we take advantage of passively-collected signals such as eye tracking or “gaze” data, to reduce the amount of hand-labeled data needed for model training.
Specifically, we leverage gaze information to directly supervise a visual attention layer by penalizing disagreement between the spatial regions the human labeler looked at the longest and those that most heavily influence model output.
We present evidence that constraining the model in this way can reduce the number of labeled examples required to achieve a given performance level by as much as 50%, and that gaze information is most helpful on more difficult tasks.

assistant: text: We explore using passively collected eye-tracking data to reduce the amount of labeled data needed during training.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: MEMO: A Deep Network for Flexible Combination of Episodic Memories

Paper content:
Recent research developing neural network architectures with external memory have often used the benchmark bAbI question and answering dataset which provides a challenging number of tasks requiring reasoning.
Here we employed a classic associative inference task from the human neuroscience literature in order to more carefully probe the reasoning capacity of existing memory-augmented architectures.
This task is thought to capture the essence of reasoning -- the appreciation of distant relationships among elements distributed across multiple facts or memories.
Surprisingly, we found that current architectures struggle to reason over long distance associations.
Similar results were obtained on a more complex task involving finding the shortest path between nodes in a path.
We therefore developed a novel architecture, MEMO, endowed with the capacity to reason over longer distances.
This was accomplished with the addition of two novel components.
First, it introduces a separation between memories/facts stored in external memory and the items that comprise these facts in external memory.
Second, it makes use of an adaptive retrieval mechanism, allowing a variable number of ‘memory hops’ before the answer is produced.
MEMO is capable of solving our novel reasoning tasks, as well as all 20 tasks in bAbI.

assistant: text: A memory architecture that support inferential reasoning.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Divide, Conquer, and Combine: a New Inference Strategy for Probabilistic Programs with Stochastic Support

Paper content:
Universal probabilistic programming systems (PPSs) provide a powerful framework for specifying rich and complex probabilistic models.
However, this expressiveness comes at the cost of substantially complicating the process of drawing inferences from the model.
In particular, inference can become challenging when the support of the model varies between executions.
Though general-purpose inference engines have been designed to operate in such settings, they are typically inefficient, often relying on proposing from the prior to make transitions.
To address this, we introduce a new inference framework: Divide, Conquer, and Combine (DCC).
DCC divides the program into separate straight-line sub-programs, each of which has a fixed support allowing more powerful inference algorithms to be run locally, before recombining their outputs in a principled fashion.
We show how DCC can be implemented as an automated and general-purpose PPS inference engine, and empirically confirm that it can provide substantial performance improvements over previous approaches.

assistant: text: Divide, Conquer, and Combine is a new inference scheme that can be performed on the probabilistic programs with stochastic support, i.e. the very existence of variables is stochastic.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Gaggle: Visual Analytics for Model Space Navigation

Paper content:
Recent visual analytics systems make use of multiple machine learning models to better fit the data as opposed to traditional single, pre-defined model systems.
However, while multi-model visual analytic systems can be effective, their added complexity poses usability concerns, as users are required to interact with the parameters of multiple models.
Further, the advent of various model algorithms and associated hyperparameters creates an exhaustive model space to sample models from.
This poses complexity to navigate this model space to find the right model for the data and the task.
In this paper, we present Gaggle, a multi-model visual analytic system that enables users to interactively navigate the model space.
Further translating user interactions into inferences, Gaggle simplifies working with multiple models by automatically finding the best model from the high-dimensional model space to support various user tasks.
Through a qualitative user study, we show how our approach helps users to find a best model for a classification and ranking task.
The study results confirm that Gaggle is intuitive and easy to use, supporting interactive model space navigation and automated model selection without requiring any technical expertise from users.

assistant: text: A new visual analytic system which aims to enable non-expert users to interactively navigate a model space by using a demonstration-based approach.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: On Meaning-Preserving Adversarial Perturbations for Sequence-to-Sequence Models

Paper content:
Adversarial examples have been shown to be an effective way of assessing the robustness of neural sequence-to-sequence (seq2seq) models, by applying perturbations to the input of a model leading to large degradation in performance.
However, these perturbations are only indicative of a weakness in the model if they do not change the semantics of the input in a way that would change the expected output.
Using the example of machine translation (MT), we propose a new evaluation framework for adversarial attacks on seq2seq models taking meaning preservation into account and demonstrate that existing methods may not preserve meaning in general.
Based on these findings, we propose new constraints for attacks on word-based MT systems and show, via human and automatic evaluation, that they produce more semantically similar adversarial inputs.
Furthermore, we show that performing adversarial training with meaning-preserving attacks is beneficial to the model in terms of adversarial robustness without hurting test performance.

assistant: text: The paper is about meaning-preserving adversarial perturbations in the context of Seq2Seq models
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: GENERATING HIGH FIDELITY IMAGES WITH SUBSCALE PIXEL NETWORKS AND MULTIDIMENSIONAL UPSCALING

Paper content:
The unconditional generation of high fidelity images is a longstanding benchmark
for testing the performance of image decoders.
Autoregressive image models
have been able to generate small images unconditionally, but the extension of
these methods to large images where fidelity can be more readily assessed has
remained an open problem.
Among the major challenges are the capacity to encode
the vast previous context and the sheer difficulty of learning a distribution that
preserves both global semantic coherence and exactness of detail.
To address the
former challenge, we propose the Subscale Pixel Network (SPN), a conditional
decoder architecture that generates an image as a sequence of image slices of equal
size.
The SPN compactly captures image-wide spatial dependencies and requires a
fraction of the memory and the computation.
To address the latter challenge, we
propose to use multidimensional upscaling to grow an image in both size and depth
via intermediate stages corresponding to distinct SPNs.
We evaluate SPNs on the
unconditional generation of CelebAHQ of size 256 and of ImageNet from size 32
to 128.
We achieve state-of-the-art likelihood results in multiple settings, set up
new benchmark results in previously unexplored settings and are able to generate
very high fidelity large scale samples on the basis of both datasets.

assistant: text: This paper addresses the problem of generation to high fidelity images, successfully showing convincing Imagenet samples with 128x128 resolution for a likelihood density model.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Beyond Classical Diffusion: Ballistic Graph Neural Network

Paper content:
This paper presents the ballistic graph neural network.
Ballistic graph neural network tackles the weight distribution from a transportation perspective and has many different properties comparing to the traditional graph neural network pipeline.
The ballistic graph neural network does not require to calculate any eigenvalue.
The filters propagate exponentially faster($\sigma^2 \sim T^2$) comparing to traditional graph neural network($\sigma^2 \sim T$).
We use a perturbed coin operator to perturb and optimize the diffusion rate.
Our results show that by selecting the diffusion speed, the network can reach a similar accuracy with fewer parameters.
We also show the perturbed filters act as better representations comparing to pure ballistic ones.
We provide a new perspective of training graph neural network, by adjusting the diffusion rate, the neural network's performance can be improved.

assistant: text: A new diffusion operation for graph neural networks that does not require eigenvalue calculation and can propagate exponentially faster compared to traditional graph neural networks.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Reinforcement Learning with Chromatic Networks

Paper content:
We present a neural architecture search algorithm to construct compact reinforcement learning (RL) policies, by combining ENAS and ES in a highly scalable and intuitive way.
By defining the combinatorial search space of NAS to be the set of different edge-partitionings (colorings) into same-weight classes, we represent compact architectures via efficient learned edge-partitionings.
For several RL tasks, we manage to learn colorings translating to effective policies parameterized by as few as 17 weight parameters, providing >90 % compression over vanilla policies and 6x compression over state-of-the-art compact policies based on Toeplitz matrices, while still maintaining good reward.
We believe that our work is one of the first attempts to propose a rigorous approach to training structured neural network architectures for RL problems that are of interest especially in mobile robotics with limited storage and computational resources.

assistant: text: This paper combines ideas from ENAS and ES methods for optimisation, and introduces the chromatic network architecture, which partitions weights of the RL network into tied sub-groups.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: The Probabilistic Fault Tolerance of Neural Networks in the Continuous Limit

Paper content:
The loss of a few neurons in a brain rarely results in any visible loss of function.
However, the insight into what “few” means in this context is unclear.
How many random neuron failures will it take to lead to a visible loss of function?
In this paper, we address the fundamental question of the impact of the crash of a random subset of neurons on the overall computation of a neural network and the error in the output it produces.
We study fault tolerance of neural networks subject to small random neuron/weight crash failures in a probabilistic setting.
We give provable guarantees on the robustness of the network to these crashes.
Our main contribution is a bound on the error in the output of a network under small random Bernoulli crashes proved by using a Taylor expansion in the continuous limit, where close-by neurons at a layer are similar.
The failure mode we adopt in our model is characteristic of neuromorphic hardware, a promising technology to speed up artificial neural networks, as well as of biological networks.
We show that our theoretical bounds can be used to compare the fault tolerance of different architectures and to design a regularizer improving the fault tolerance of a given architecture.
We design an algorithm achieving fault tolerance using a reasonable number of neurons.
In addition to the theoretical proof, we also provide experimental validation of our results and suggest a connection to the generalization capacity problem.

assistant: text: This paper considers the problem of dropping neurons from a neural network, showing that if the goal is to become robust to randomly dropped neurons during evaluation, then it is sufficient to just train with dropout.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Training for Faster Adversarial Robustness Verification via Inducing ReLU Stability

Paper content:
We explore the concept of co-design in the context of neural network verification.
Specifically, we aim to train deep neural networks that not only are robust to adversarial perturbations but also whose robustness can be verified more easily.
To this end, we identify two properties of network models - weight sparsity and so-called ReLU stability - that turn out to significantly impact the complexity of the corresponding verification task.
We demonstrate that improving weight sparsity alone already enables us to turn computationally intractable verification problems into tractable ones.
Then, improving ReLU stability leads to an additional 4-13x speedup in verification times.
An important feature of our methodology is its "universality," in the sense that it can be used with a broad range of training procedures and verification approaches.

assistant: text: This paper proposes methods to train robust neural networks that can be verified faster, using pruning methods to encourage weight sparsity and regularization to encourage ReLU stability.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: EXPLORING DEEP LEARNING USING INFORMATION THEORY TOOLS AND PATCH ORDERING

Paper content:
We present a framework for automatically ordering image patches that enables in-depth analysis of dataset relationship to learnability of a classification task using convolutional neural network.
An image patch is a group of pixels residing in a continuous area contained in the sample.
Our preliminary experimental results show that an informed smart shuffling of patches at a sample level can expedite training by exposing important features at early stages of training.
In addition, we conduct systematic experiments and provide evidence that CNN’s generalization capabilities do not correlate with human recognizable features present in training samples.
We utilized the framework not only to show that spatial locality of features within samples do not correlate with generalization, but also to expedite convergence while achieving similar generalization performance.
Using multiple network architectures and datasets, we show that ordering image regions using mutual information measure between adjacent patches, enables CNNs to converge in a third of the total steps required to train the same network without patch ordering.

assistant: text: Develop new techniques that rely on patch reordering to enable detailed analysis of data-set relationship to training and generalization performances.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: DeepErase: Weakly Supervised Ink Artifact Removal in Document Text Images

Paper content:
Still in 2019, many scanned documents come into businesses in non-digital format.
Text to be extracted from real world documents is often nestled inside rich formatting, such as tabular structures or forms with fill-in-the-blank boxes or underlines whose ink often touches or even strikes through the ink of the text itself.
Such ink artifacts can severely interfere with the performance of recognition algorithms or other downstream processing tasks.
In this work, we propose DeepErase, a neural preprocessor to erase ink artifacts from text images.
We devise a method to programmatically augment text images with real artifacts, and use them to train a segmentation network in an weakly supervised manner.
In additional to high segmentation accuracy, we show that our cleansed images achieve a significant boost in downstream recognition accuracy by popular OCR software such as Tesseract 4.0.
We test DeepErase on out-of-distribution datasets (NIST SDB) of scanned IRS tax return forms and achieve double-digit improvements in recognition accuracy over baseline for both printed and handwritten text.

assistant: text: Neural-based removal of document ink artifacts (underlines, smudges, etc.) using no manually annotated training data
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: A Simple Geometric Proof for the Benefit of Depth in ReLU Networks

Paper content:
We present a simple proof for the benefit of depth in multi-layer feedforward network with rectifed activation (``"depth separation").
Specifically we present a sequence of classification problems f_i such that
(a) for any fixed depth rectified network we can find an index m such that problems with index > m require exponential network width to fully represent the function f_m; and
(b) for any problem f_m in the family, we present a concrete neural network with linear depth and bounded width that fully represents it.
While there are several previous work showing similar results, our proof uses substantially simpler tools and techniques, and should be accessible to undergraduate students in computer science and people with similar backgrounds.
We present a simple, geometric proof of the benefit of depth in deep neural networks.
We prove that there exist a set of functions indexed by m, each of which can be efficiently represented by a depth m rectified MLP network requiring O(m) parameters.
However, for any bounded depth rectified MLP network, there is a function f m in this set that representing it will require an exponential number of parameters in m.
More formally, let G d be the set of multi-layer perceptron (MLP) networks with rectified activation and d hidden layers, and let g Θ be such an MLP with parameters Θ.
We will prove the following theorem: Theorem 1 (Depth Separation).
There exists a set of functions f 1 , f 2 , ..., f i : R 2 → {−1, 1} such that: While this is not a novel result, a main characteristic of our proof is its simplicity.
In contrast to previous work, our proof uses only basic algebra, geometry and simple combinatorial arguments.
As such, it can be easily read and understood by newcomers and practitioners, or taught in an undergraduate class, without requiring extensive background.
Tailoring to these crowds, our presentation style is more verbose then is usual in papers of this kind, attempting to spell out all steps explicitly.
We also opted to trade generality for proof simplicity, remaining in input space R 2 rather than the more general R n , thus allowing us to work with lines rather than hyperplanes.
Beyond being easy to visualize, it also results in simple proofs of the different lemmas.

assistant: text: ReLU MLP depth seperation proof with gemoteric arguments
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Bayesian Residual Policy Optimization: Scalable Bayesian Reinforcement Learning with Clairvoyant Experts

Paper content:
Informed and robust decision making in the face of uncertainty is critical for robots that perform physical tasks alongside people.
We formulate this as a Bayesian Reinforcement Learning problem over latent Markov Decision Processes (MDPs).
While Bayes-optimality is theoretically the gold standard, existing algorithms do not scale well to continuous state and action spaces.
We propose a scalable solution that builds on the following insight: in the absence of uncertainty, each latent MDP is easier to solve.
We split the challenge into two simpler components.
First, we obtain an ensemble of clairvoyant experts and fuse their advice to compute a baseline policy.
Second, we train a Bayesian residual policy to improve upon the ensemble's recommendation and learn to reduce uncertainty.
Our algorithm, Bayesian Residual Policy Optimization (BRPO), imports the scalability of policy gradient methods as well as the initialization from prior models.
BRPO significantly improves the ensemble of experts and drastically outperforms existing adaptive RL methods.

assistant: text: We propose a scalable Bayesian Reinforcement Learning algorithm that learns a Bayesian correction over an ensemble of clairvoyant experts to solve problems with complex latent rewards and dynamics.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Discriminative Particle Filter Reinforcement Learning for Complex Partial observations

Paper content:
Deep reinforcement learning has succeeded in sophisticated games such as Atari, Go, etc.
Real-world decision making, however, often requires reasoning with partial information extracted from complex visual observations.
This paper presents  Discriminative Particle Filter Reinforcement Learning (DPFRL), a new reinforcement learning framework for partial and complex observations.
DPFRL encodes a differentiable particle filter with learned transition and observation models in a neural network, which allows for reasoning with partial observations over multiple time steps.
While a standard particle filter relies on a generative observation model, DPFRL learns a discriminatively parameterized model that is training directly for decision making.
We show that the discriminative parameterization results in significantly improved performance, especially for tasks with complex visual observations, because it circumvents the difficulty of modelling observations explicitly.
In most cases, DPFRL outperforms state-of-the-art POMDP RL models in Flickering Atari Games, an existing POMDP RL benchmark, and in Natural Flickering Atari Games, a new, more challenging POMDP RL benchmark that we introduce.
We further show that DPFRL performs well for visual navigation with real-world data.

assistant: text: Introduces ideas for training DLR agents with latent state variables, modeled as a belief distribution, so they can handle partially observed environments.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Efficient Deep Representation Learning by Adaptive Latent Space Sampling

Paper content:
Supervised deep learning requires a large amount of training samples with annotations (e.g. label class for classification task, pixel- or voxel-wised label map for segmentation tasks), which are expensive and time-consuming to obtain.
During the training of a deep neural network, the annotated samples are fed into the network in a mini-batch way, where they are often regarded of equal importance.
However, some of the samples may become less informative during training, as the magnitude of the gradient start to vanish for these samples.
In the meantime, other samples of higher utility or hardness may be more demanded for the training process to proceed and require more exploitation.
To address the challenges of expensive annotations and loss of sample informativeness, here we propose a novel training framework which adaptively selects informative samples that are fed to the training process.
The adaptive selection or sampling is performed based on a hardness-aware strategy in the latent space constructed by a generative model.
To evaluate the proposed training framework, we perform experiments on three different datasets, including MNIST and CIFAR-10 for image classification task and a medical image dataset IVUS for biophysical simulation task.
On all three datasets, the proposed framework outperforms a random sampling method, which demonstrates the effectiveness of our framework.

assistant: text: A method for sequential and adaptive selection of training examples to be presented to the training algorithm, where selection happens in the latent space based on choosing samples in the direction of the gradient of the loss.