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

Title: The Cramer Distance as a Solution to Biased Wasserstein Gradients

Paper content:
The Wasserstein probability metric has received much attention from the machine learning community.
Unlike the Kullback-Leibler divergence, which strictly measures change in probability, the Wasserstein metric reflects the underlying geometry between outcomes.
The value of being sensitive to this geometry has been demonstrated, among others, in ordinal regression and generative modelling, and most recently in reinforcement learning.
In this paper we describe three natural properties of probability divergences that we believe reflect requirements from machine learning: sum invariance, scale sensitivity, and unbiased sample gradients.
The Wasserstein metric possesses the first two properties but, unlike the Kullback-Leibler divergence, does not possess the third.
We provide empirical evidence suggesting this is a serious issue in practice.
Leveraging insights from probabilistic forecasting we propose an alternative to the Wasserstein metric, the Cramér distance.
We show that the Cramér distance possesses all three desired properties, combining the best of the Wasserstein and Kullback-Leibler divergences.
We give empirical results on a number of domains comparing these three divergences.
To illustrate the practical relevance of the Cramér distance we design a new algorithm, the Cramér Generative Adversarial Network (GAN), and show that it has a number of desirable properties over the related Wasserstein GAN.

assistant: text: The contribution of the article is related to performance criteria, in particular to the Wasserstein/Mallows metric
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: TabNN: A Universal Neural Network Solution for Tabular Data

Paper content:
Neural Network (NN) has achieved state-of-the-art performances in many tasks within image, speech, and text domains.
Such great success is mainly due to special structure design to fit the particular data patterns, such as CNN capturing spatial locality and RNN modeling sequential dependency.
Essentially, these specific NNs achieve good performance by leveraging the prior knowledge over corresponding domain data.
Nevertheless, there are many applications with all kinds of tabular data in other domains.
Since there are no shared patterns among these diverse tabular data, it is hard to design specific structures to fit them all.
Without careful architecture design based on domain knowledge, it is quite challenging for NN to reach satisfactory performance in these tabular data domains.
To fill the gap of NN in tabular data learning, we propose a universal neural network solution, called TabNN, to derive effective NN architectures for tabular data in all kinds of tasks automatically.
Specifically, the design of TabNN follows two principles: \emph{to explicitly leverages expressive feature combinations} and \emph{to reduce model complexity}.
Since GBDT has empirically proven its strength in modeling tabular data, we use GBDT to power the implementation of TabNN.
Comprehensive experimental analysis on a variety of tabular datasets demonstrate that TabNN can achieve much better performance than many baseline solutions.

assistant: text: Proposal for a hybrid machine learning algorithm using Gradient Boosted Decision Trees and Deep Neural Networks, with intended research direction on tabular data.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Discrete Transformer

Paper content:
The transformer has become a central model for many NLP tasks from translation to language modeling to representation learning.
Its success demonstrates the effectiveness of stacked attention as a replacement for recurrence for many tasks.
In theory attention also offers more insights into the model’s internal decisions; however, in practice when stacked it quickly becomes nearly as fully-connected as recurrent models.
In this work, we propose an alternative transformer architecture, discrete transformer, with the goal of better separating out internal model decisions.
The model uses hard attention to ensure that each step only depends on a fixed context.
Additionally, the model uses a separate “syntactic” controller to separate out network structure from decision making.
Finally we show that this approach can be further sparsified with direct regularization.
Empirically, this approach is able to maintain the same level of performance on several datasets, while discretizing reasoning decisions over the data.

assistant: text: This paper presents modifications to the standard transformer architecture with the goal of improving interpretability while retaining performance in NLP tasks.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Explaining the Mistakes of Neural Networks with Latent Sympathetic Examples

Paper content:
Neural networks make mistakes.
The reason why a mistake is made often remains a mystery.
As such neural networks often are considered a black box.
It would be useful to have a method that can give an explanation that is intuitive to a user as to why an image is misclassified.
In this paper we develop a method for explaining the mistakes of a classifier model by visually showing what must be added to an image such that it is correctly classified.
Our work combines the fields of adversarial examples, generative modeling and a correction technique based on difference target propagation to create an technique that creates explanations of why an image is misclassified.
In this paper we explain our method and demonstrate it on MNIST and CelebA.
This approach could aid in demystifying neural networks for a user.

assistant: text: This paper proposes a method for explaining the classification mistakes of neural networks.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Better Knowledge Retention through Metric Learning

Paper content:
In a continual learning setting, new categories may be introduced over time, and an ideal learning system should perform well on both the original categories and the new categories.
While deep neural nets have achieved resounding success in the classical setting, they are known to forget about knowledge acquired in prior episodes of learning if the examples encountered in the current episode of learning are drastically different from those encountered in prior episodes.
This makes deep neural nets ill-suited to continual learning.
In this paper, we propose a new model that can both leverage the expressive power of deep neural nets and is resilient to forgetting when new categories are introduced.
We demonstrate an improvement in terms of accuracy on original classes compared to a vanilla deep neural net.

assistant: text: This paper applies metric learning to reduce catastrophic forgetting on neural networks by improving the expressiveness of the final layer, leading to better results in continual learning.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Restricting the Flow: Information Bottlenecks for Attribution

Paper content:
Attribution methods provide insights into the decision-making of machine learning models like artificial neural networks.
For a given input sample, they assign a relevance score to each individual input variable, such as the pixels of an image.
In this work we adapt the information bottleneck concept for attribution.
By adding noise to intermediate feature maps we restrict the flow of information and can quantify (in bits) how much information image regions provide.
We compare our method against ten baselines using three different metrics on VGG-16 and ResNet-50, and find that our methods outperform all baselines in five out of six settings.
The method’s information-theoretic foundation provides an absolute frame of reference for attribution values (bits) and a guarantee that regions scored close to zero are not necessary for the network's decision.

assistant: text: We apply the informational bottleneck concept to attribution.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: META LEARNING SHARED HIERARCHIES

Paper content:
We develop a metalearning approach for learning hierarchically structured poli- cies, improving sample efficiency on unseen tasks through the use of shared primitives—policies that are executed for large numbers of timesteps.
Specifi- cally, a set of primitives are shared within a distribution of tasks, and are switched between by task-specific policies.
We provide a concrete metric for measuring the strength of such hierarchies, leading to an optimization problem for quickly reaching high reward on unseen tasks.
We then present an algorithm to solve this problem end-to-end through the use of any off-the-shelf reinforcement learning method, by repeatedly sampling new tasks and resetting task-specific policies.
We successfully discover meaningful motor primitives for the directional movement of four-legged robots, solely by interacting with distributions of mazes.
We also demonstrate the transferability of primitives to solve long-timescale sparse-reward obstacle courses, and we enable 3D humanoid robots to robustly walk and crawl with the same policy.

assistant: text: The authors consider the problem of learning a useful set of ‘sub policies’ that can be shared between tasks so as to jump start learning on new tasks drawn from the task distribution.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Consistency-based anomaly detection with adaptive multiple-hypotheses predictions

Paper content:
In one-class-learning tasks, only the normal case can be modeled with data, whereas the variation of all possible anomalies is too large to be described sufficiently by samples.
Thus, due to the lack of representative data, the wide-spread discriminative approaches cannot cover such learning tasks, and rather generative models, which attempt to learn the input density of the normal cases, are used.
However, generative models suffer from a large input dimensionality (as in images) and are typically inefficient learners.
We propose to learn the data distribution more efficiently with a multi-hypotheses autoencoder.
Moreover, the model is criticized by a discriminator, which prevents artificial data modes not supported by data, and which enforces diversity across hypotheses.
This consistency-based anomaly detection (ConAD) framework allows the reliable identification of outof- distribution samples.
For anomaly detection on CIFAR-10, it yields up to 3.9% points improvement over previously reported results.
On a real anomaly detection task, the approach reduces the error of the baseline models from 6.8% to 1.5%.

assistant: text: The paper proposes a technique to make generative models more robust by making them consistent with the local density.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Adaptive Estimators Show Information Compression in Deep Neural Networks

Paper content:
To improve how neural networks function it is crucial to understand their learning process.
The information bottleneck theory of deep learning proposes that neural networks achieve good generalization by compressing their representations to disregard information that is not relevant to the task.
However, empirical evidence for this theory is conflicting, as compression was only observed when networks used saturating activation functions.
In contrast, networks with non-saturating activation functions achieved comparable levels of task performance but did not show compression.
In this paper we developed more robust mutual information estimation techniques, that adapt to hidden activity of neural networks and produce more sensitive measurements of activations from all functions, especially unbounded functions.
Using these adaptive estimation techniques, we explored compression in networks with a range of different activation functions.
With two improved methods of estimation, firstly, we show that saturation of the activation function is not required for compression, and the amount of compression varies between different activation functions.
We also find that there is a large amount of variation in compression between different network initializations.
Secondary, we see that L2 regularization leads to significantly increased compression, while preventing overfitting.
Finally, we show that only compression of the last layer is positively correlated with generalization.

assistant: text: This paper proposes a method for the estimation of mutual information for networks with unbounded activation functions and the use of L2 regularization to induce more compression.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Robust Text Classifier on Test-Time Budgets

Paper content:
In this paper, we design a generic framework for learning a robust text classification model that achieves accuracy comparable to standard full models under test-time
budget constraints.
We take a different approach from existing methods and learn to dynamically delete a large fraction of unimportant words by a low-complexity selector such that the high-complexity classifier only needs to process a small fraction of important words.
In addition, we propose a new data aggregation method to train the classifier, allowing it to make accurate predictions even on fragmented sequence of words.
Our end-to-end method achieves state-of-the-art performance while its computational complexity scales linearly with the small fraction of important words in the whole corpus.
Besides, a single deep neural network classifier trained by our framework can be dynamically tuned to different budget levels at inference time.

assistant: text: Modular framework for document classification and data aggregation technique for making the framework robust to various distortion, and noise and focus only on the important words.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Sparse Coding with Gated Learned ISTA

Paper content:
In this paper, we study the learned iterative shrinkage thresholding algorithm (LISTA) for solving sparse coding problems.
Following assumptions made by prior works, we first discover that the code components in its estimations may be lower than expected, i.e., require gains, and to address this problem, a gated mechanism amenable to theoretical analysis is then introduced.
Specific design of the gates is inspired by convergence analyses of the mechanism and hence its effectiveness can be formally guaranteed.
In addition to the gain gates, we further introduce overshoot gates for compensating insufficient step size in LISTA.
Extensive empirical results confirm our theoretical findings and verify the effectiveness of our method.

assistant: text: We propose gated mechanisms to enhance learned ISTA for sparse coding, with theoretical guarantees on the superiority of the method.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Fluctuation-dissipation relations for stochastic gradient descent

Paper content:
The notion of the stationary equilibrium ensemble has played a central role in statistical mechanics.
In machine learning as well, training serves as generalized equilibration that drives the probability distribution of model parameters toward stationarity.
Here, we derive stationary fluctuation-dissipation relations that link measurable quantities and hyperparameters in the stochastic gradient descent algorithm.
These relations hold exactly for any stationary state and can in particular be used to adaptively set training schedule.
We can further use the relations to efficiently extract information pertaining to a loss-function landscape such as the magnitudes of its Hessian and anharmonicity.
Our claims are empirically verified.

assistant: text: We prove fluctuation-dissipation relations for SGD, which can be used to (i) adaptively set learning rates and (ii) probe loss surfaces.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Decoupling Hierarchical Recurrent Neural Networks With Locally Computable Losses

Paper content:
Learning long-term dependencies is a key long-standing challenge of recurrent neural networks (RNNs).
Hierarchical recurrent neural networks (HRNNs) have been considered a promising approach as long-term dependencies are resolved through shortcuts up and down the hierarchy.
Yet, the memory requirements of Truncated Backpropagation Through Time (TBPTT) still prevent training them on very long sequences.
In this paper, we empirically show that in (deep) HRNNs, propagating gradients back from higher to lower levels can be replaced by locally computable losses, without harming the learning capability of the network, over a wide range of tasks.
This decoupling by local losses reduces the memory requirements of training by a factor exponential in the depth of the hierarchy in comparison to standard TBPTT.

assistant: text: The proposed paper suggests to decouple the different layers of hierarchy in RNN using auxiliary losses.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Adaptive Structural Fingerprints for Graph Attention Networks

Paper content:
Many real-world data sets are represented as graphs, such as citation links, social media, and biological interaction.
The volatile graph structure makes it non-trivial to employ convolutional neural networks (CNN's) for graph data processing.
Recently, graph attention network (GAT) has proven a promising attempt by combining graph neural networks with attention mechanism, so as to achieve massage passing in graphs with arbitrary structures.
However, the attention in GAT is computed mainly based on the similarity between the node content, while the structures of the graph remains largely unemployed (except in masking the attention out of one-hop neighbors).
In this paper, we propose an `````````````````````````````"ADaptive Structural Fingerprint" (ADSF) model to fully exploit both topological details of the graph and  content features of the nodes.
The key idea is to contextualize each node with a weighted, learnable receptive field  encoding rich and diverse local graph structures.
By doing this, structural interactions between the nodes can  be inferred accurately, thus improving subsequent attention layer as well as the convergence of learning.
Furthermore, our model provides a useful platform  for different subspaces of node features and various scales of graph structures to ``cross-talk'' with each other through the learning of multi-head attention, being particularly useful in handling complex real-world data.
Encouraging performance is observed on a number of benchmark data sets in node classification.

assistant: text: This paper extends the idea of self-attention in graph NNs, which is typically based on feature similarity between nodes, to include structural similarity.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Rethinking learning rate schedules for stochastic optimization

Paper content:
There is a stark disparity between the learning rate schedules used in the practice of large scale machine learning and what are considered admissible learning rate schedules prescribed in the theory of stochastic approximation.
Recent results, such as in the 'super-convergence' methods which use oscillating learning rates, serve to emphasize this point even more.
One plausible explanation is that non-convex neural network training procedures are better suited to the use of fundamentally different learning rate  schedules, such as the ``cut the learning rate every constant number of epochs'' method (which more closely resembles an exponentially decaying learning rate schedule); note that this widely used schedule is in stark contrast to the polynomial decay schemes prescribed in the stochastic approximation literature, which are indeed shown to be (worst case) optimal for classes of convex optimization problems.
The main contribution of this work shows that the picture is far more nuanced, where we do not even need to move to non-convex optimization to show other learning rate schemes can be far more effective.
In fact, even for the simple case of stochastic linear regression with a fixed time horizon, the rate achieved by any polynomial decay scheme is sub-optimal compared to the statistical minimax rate (by a factor of condition number); in contrast the ```''cut the learning rate every constant number of epochs'' provides an exponential improvement (depending only logarithmically on the condition number) compared to any polynomial decay scheme.
Finally, it is important to ask if our theoretical insights are somehow fundamentally tied to quadratic loss minimization (where we have circumvented minimax lower bounds for more general convex optimization problems)?
Here, we conjecture that recent results which make the gradient norm small at a near optimal rate, for both convex and non-convex optimization, may also provide more insights into learning rate schedules used in practice.

assistant: text: This paper presents a rigorous study of why practically used learning rate schedules (for a given computational budget) offer significant advantages even though these schemes are not advocated by the classical theory of Stochastic Approximation.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Min-max Entropy for Weakly Supervised Pointwise Localization

Paper content:
Pointwise localization allows more precise localization and accurate interpretability, compared to bounding box, in applications where objects are highly unstructured such as in medical domain.
In this work, we focus on  weakly supervised localization (WSL) where a model is trained to classify an image and localize regions of interest at pixel-level using only global image annotation.
Typical convolutional attentions maps are prune to high false positive regions.
To alleviate this issue, we propose a new deep learning method for WSL, composed of a localizer and a classifier, where the localizer is constrained to determine relevant and irrelevant regions using conditional entropy (CE) with the aim to reduce false positive regions.
Experimental results on a public medical dataset and two natural datasets, using Dice index, show that, compared to state of the art WSL methods, our proposal can provide significant improvements in terms of image-level classification and pixel-level localization (low false positive) with robustness to overfitting.
A public reproducible PyTorch implementation is provided.

assistant: text: A deep learning method for weakly-supervised pointwise localization that learns using image-level label only. It relies on conditional entropy to localize relevant and irrelevant regions aiming to minimize false positive regions.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Neural Execution of Graph Algorithms

Paper content:
Graph Neural Networks (GNNs) are a powerful representational tool for solving problems on graph-structured inputs.
In almost all cases so far, however, they have been applied to directly recovering a final solution from raw inputs, without explicit guidance on how to structure their problem-solving.
Here, instead, we focus on learning in the space of algorithms: we train several state-of-the-art GNN architectures to imitate individual steps of classical graph algorithms, parallel (breadth-first search, Bellman-Ford) as well as sequential (Prim's algorithm).
As graph algorithms usually rely on making discrete decisions within neighbourhoods, we hypothesise that maximisation-based message passing neural networks are best-suited for such objectives, and validate this claim empirically.
We also demonstrate how learning in the space of algorithms can yield new opportunities for positive transfer between tasks---showing how learning a shortest-path algorithm can be substantially improved when simultaneously learning a reachability algorithm.

assistant: text: We supervise graph neural networks to imitate intermediate and step-wise outputs of classical graph algorithms, recovering highly favourable insights.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: A Seed-Augment-Train Framework for Universal Digit Classification 

Paper content:
In this paper, we propose a Seed-Augment-Train/Transfer (SAT) framework that contains a synthetic seed image dataset generation procedure for languages with different numeral systems using freely available open font file datasets.
This seed dataset of images is then augmented to create a purely synthetic training dataset, which is in turn used to train a deep neural network and test on held-out real world handwritten digits dataset spanning five Indic scripts, Kannada, Tamil, Gujarati, Malayalam, and Devanagari.
We showcase the efficacy of this approach both qualitatively, by training a Boundary-seeking GAN (BGAN) that generates realistic digit images in the five languages, and also qualitatively by testing a CNN trained on the synthetic data on the real-world datasets.
This establishes not only an interesting nexus between the font-datasets-world and transfer learning but also provides a recipe for universal-digit classification in any script.

assistant: text: This paper presents new datasets for five languages and proposes a new framework (SAT) for font image datasets generation for universal digit classification.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Automatically Inferring Data Quality for Spatiotemporal Forecasting

Paper content:
Spatiotemporal forecasting has become an increasingly important prediction task in machine learning and statistics due to its vast applications, such as climate modeling, traffic prediction, video caching predictions, and so on.
While numerous studies have been conducted, most existing works assume that the data from different sources or across different locations are equally reliable.
Due to cost, accessibility, or other factors, it is inevitable that the data quality could vary, which introduces significant biases into the model and leads to unreliable prediction results.
The problem could be exacerbated in black-box prediction models, such as deep neural networks.
In this paper, we propose a novel solution that can automatically infer data quality levels of different sources through local variations of spatiotemporal signals without explicit labels.
Furthermore, we integrate the estimate of data quality level with graph convolutional networks to exploit their efficient structures.
We evaluate our proposed method on forecasting temperatures in Los Angeles.

assistant: text: Introduces a new definition of data quality that relies on the notion of local variation defined in (Zhou and Scholkopf) and extends it to multiple heterogenous data sources.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Intrinsic Motivation and Automatic Curricula via Asymmetric Self-Play

Paper content:
We describe a simple scheme that allows an agent to learn about its environment in an unsupervised manner.
Our scheme pits two versions of the same agent, Alice and Bob, against one another.
Alice proposes a task for Bob to complete; and then Bob attempts to complete the task.
In this work we will focus on two kinds of environments: (nearly) reversible environments and environments that can be reset.
Alice will "propose" the task by doing a sequence of actions and then Bob must undo or repeat them, respectively.
Via an appropriate reward structure, Alice and Bob automatically generate a curriculum of exploration, enabling unsupervised training of the agent.
When Bob is deployed on an RL task within the environment, this unsupervised training reduces the number of supervised episodes needed to learn, and in some cases converges to a higher reward.

assistant: text: Unsupervised learning for reinforcement learning using an automatic curriculum of self-play
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Style Memory: Making a Classifier Network Generative

Paper content:
Deep networks have shown great performance in classification tasks.
However, the parameters learned by the classifier networks usually discard stylistic information of the input, in favour of information strictly relevant to classification.
We introduce a network that has the capacity to do both classification and reconstruction by adding a "style memory" to the output layer of the network.
We also show how to train such a neural network as a deep multi-layer autoencoder, jointly minimizing both classification and reconstruction losses.
The generative capacity of our network demonstrates that the combination of style-memory neurons with the classifier neurons yield good reconstructions of the inputs when the classification is correct.
We further investigate the nature of the style memory, and how it relates to composing digits and letters.

assistant: text: This paper proposes to train a classifier neural network not just to classifiy, but also to reconstruct a representation of its input, in order to factorize the class information from the appearance .
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Generating Biased Datasets for Neural Natural Language Processing

Paper content:
In a time where neural networks are increasingly adopted in sensitive applications, algorithmic bias has emerged as an issue with moral implications.
While there are myriad ways that a system may be compromised by bias, systematically isolating and evaluating existing systems on such scenarios is non-trivial, i.e., bias may be subtle, natural and inherently difficult to quantify.
To this end, this paper proposes the first systematic study of benchmarking state-of-the-art neural models against biased scenarios.
More concretely, we postulate that the bias annotator problem can be approximated with neural models, i.e., we propose generative models of latent bias to deliberately and unfairly associate latent features to a specific class.
All in all, our framework provides a new way for principled quantification and evaluation of models against biased datasets.
Consequently, we find that state-of-the-art NLP models (e.g., BERT, RoBERTa, XLNET) are readily compromised by biased data.

assistant: text: A method to generate biased datasets for NLP, relying on a conditional adversarially regularized autoencoder (CARA).
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Split LBI for Deep Learning: Structural Sparsity via Differential Inclusion Paths

Paper content:
Over-parameterization is ubiquitous nowadays in training neural networks to benefit both optimization in seeking global optima and generalization in reducing prediction error.
However, compressive networks are desired in many real world applications and direct training of small networks may be trapped in local optima.
In this paper, instead of pruning or distilling over-parameterized models to compressive ones, we propose a new approach based on \emph{differential inclusions of inverse scale spaces}, that generates a family of models from simple to complex ones by coupling gradient descent and mirror descent to explore model structural sparsity.
It has a simple discretization, called the Split Linearized Bregman Iteration (SplitLBI), whose global convergence analysis in deep learning is established that from any initializations, algorithmic iterations converge to a critical point of empirical risks.
Experimental evidence shows that\ SplitLBI may achieve state-of-the-art performance in large scale training on ImageNet-2012 dataset etc., while with \emph{early stopping} it unveils effective subnet architecture with comparable test accuracies to dense models after retraining instead of pruning well-trained ones.

assistant: text: Proposes an optimization based algorithm for finding important sparse structures of large-scale neural networks by coupling the learning of weight matrix and sparsity constraints, offering guaranteed convergence on nonconvex optimization problems.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Evaluating Robustness of Neural Networks with Mixed Integer Programming

Paper content:
Neural networks trained only to optimize for training accuracy can often be fooled by adversarial examples --- slightly perturbed inputs misclassified with high confidence.
Verification of networks enables us to gauge their vulnerability to such adversarial examples.
We formulate verification of piecewise-linear neural networks as a mixed integer program.
On a representative task of finding minimum adversarial distortions, our verifier is two to three orders of magnitude quicker than the state-of-the-art.
We achieve this computational speedup via tight formulations for non-linearities, as well as a novel presolve algorithm that makes full use of all information available.
The computational speedup allows us to verify properties on convolutional and residual networks with over 100,000 ReLUs --- several orders of magnitude more than networks previously verified by any complete verifier.
In particular, we determine for the first time the exact adversarial accuracy of an MNIST classifier to perturbations with bounded l-∞ norm ε=0.1: for this classifier, we find an adversarial example for 4.38% of samples, and a certificate of robustness to norm-bounded perturbations for the remainder.
Across all robust training procedures and network architectures considered, and for both the MNIST and CIFAR-10 datasets, we are able to certify more samples than the state-of-the-art and find more adversarial examples than a strong first-order attack.

assistant: text: We efficiently verify the robustness of deep neural models with over 100,000 ReLUs, certifying more samples than the state-of-the-art and finding more adversarial examples than a strong first-order attack.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Prestopping: How Does Early Stopping Help Generalization Against Label Noise?

Paper content:
Noisy labels are very common in real-world training data, which lead to poor generalization on test data because of overfitting to the noisy labels.
In this paper, we claim that such overfitting can be avoided by "early stopping" training a deep neural network before the noisy labels are severely memorized.
Then, we resume training the early stopped network using a "maximal safe set," which maintains a collection of almost certainly true-labeled samples at each epoch since the early stop point.
Putting them all together, our novel two-phase training method, called Prestopping, realizes noise-free training under any type of label noise for practical use.
Extensive experiments using four image benchmark data sets verify that our method significantly outperforms four state-of-the-art methods in test error by 0.4–8.2 percent points under existence of real-world noise.

assistant: text: This paper proposes a two-phase training method for learning with label noise.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Accelerating Convolutional Neural Networks using Iterative Two-Pass Decomposition

Paper content:
We present the iterative two-pass decomposition flow to accelerate existing convolutional neural networks (CNNs).
The proposed rank selection algorithm can effectively determine the proper ranks of the target convolutional layers for the low rank approximation.
Our two-pass CP-decomposition helps prevent from the instability problem.
The iterative flow makes the decomposition of the deeper networks systematic.
The experiment results shows that VGG16 can be accelerated with a 6.2x measured speedup while the accuracy drop remains only 1.2%.

assistant: text: We present the iterative two-pass CP decomposition flow to effectively accelerate existing convolutional neural networks (CNNs).
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Word2net: Deep Representations of Language

Paper content:
Word embeddings extract semantic features of words from large datasets of text.
Most embedding methods rely on a log-bilinear model to predict the occurrence
of a word in a context of other words.
Here we propose word2net, a method that
replaces their linear parametrization with neural networks.
For each term in the
vocabulary, word2net posits a neural network that takes the context as input and
outputs a probability of occurrence.
Further, word2net can use the hierarchical
organization of its word networks to incorporate additional meta-data, such as
syntactic features, into the embedding model.
For example, we show how to share
parameters across word networks to develop an embedding model that includes
part-of-speech information.
We study word2net with two datasets, a collection
of Wikipedia articles and a corpus of U.S. Senate speeches.
Quantitatively, we
found that word2net outperforms popular embedding methods on predicting held-
out words and that sharing parameters based on part of speech further boosts
performance.
Qualitatively, word2net learns interpretable semantic representations
and, compared to vector-based methods, better incorporates syntactic information.

assistant: text: Word2net is a novel method for learning neural network representations of words that can use syntactic information to learn better semantic features.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: BlackMarks: Black-box Multi-bit Watermarking for Deep Neural Networks

Paper content:
Deep Neural Networks (DNNs) are increasingly deployed in cloud servers and autonomous agents due to their superior performance.
The deployed DNN is either leveraged in a white-box setting (model internals are publicly known) or a black-box setting (only model outputs are known) depending on the application.
A practical concern in the rush to adopt DNNs is protecting the models against Intellectual Property (IP) infringement.
We propose BlackMarks, the first end-to-end multi-bit watermarking framework that is applicable in the black-box scenario.
BlackMarks takes the pre-trained unmarked model and the owner’s binary signature as inputs.
The output is the corresponding marked model with specific keys that can be later used to trigger the embedded watermark.
To do so, BlackMarks first designs a model-dependent encoding scheme that maps all possible classes in the task to bit ‘0’ and bit ‘1’.
Given the owner’s watermark signature (a binary string), a set of key image and label pairs is designed using targeted adversarial attacks.
The watermark (WM) is then encoded in the distribution of output activations of the DNN by fine-tuning the model with a WM-specific regularized loss.
To extract the WM, BlackMarks queries the model with the WM key images and decodes the owner’s signature from the corresponding predictions using the designed encoding scheme.
We perform a comprehensive evaluation of BlackMarks’ performance on MNIST, CIFAR-10, ImageNet datasets and corroborate its effectiveness and robustness.
BlackMarks preserves the functionality of the original DNN and incurs negligible WM embedding overhead as low as 2.054%.

assistant: text: The paper proposes an approach for model watermarking where the watermark is a bit string embedded in the model as part of a fine-tuning procedure
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Estimating Heterogeneous Treatment Effects Using Neural Networks With The Y-Learner

Paper content:
We develop the Y-learner for estimating heterogeneous treatment effects in experimental and observational studies.
The Y-learner is designed to leverage the abilities of neural networks to optimize multiple objectives and continually update, which allows for better pooling of underlying feature information between treatment and control groups.
We evaluate the Y-learner on three test problems: (1) A set of six simulated data benchmarks from the literature.
(2) A real-world large-scale experiment on voter persuasion.
(3) A task from the literature that estimates artificially generated treatment effects on MNIST didgits.
The Y-learner achieves state of the art results on two of the three tasks.
On the MNIST task, it gets the second best results.

assistant: text: Shows improvements to X-learner by modeling the treatment response function, the control response function, and the mapping from imputed treatment effect to the conditional average treatment effect, as neural networks.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Learning to Infer and Execute 3D Shape Programs

Paper content:
Human perception of 3D shapes goes beyond reconstructing them as a set of points or a composition of geometric primitives: we also effortlessly understand higher-level shape structure such as the repetition and reflective symmetry of object parts.
In contrast, recent advances in 3D shape sensing focus more on low-level geometry but less on these higher-level relationships.
In this paper, we propose 3D shape programs, integrating bottom-up recognition systems with top-down, symbolic program structure to capture both low-level geometry and high-level structural priors for 3D shapes.
Because there are no annotations of shape programs for real shapes, we develop neural modules that not only learn to infer 3D shape programs from raw, unannotated shapes, but also to execute these programs for shape reconstruction.
After initial bootstrapping, our end-to-end differentiable model learns 3D shape programs by reconstructing shapes in a self-supervised manner.
Experiments demonstrate that our model accurately infers and executes 3D shape programs for highly complex shapes from various categories.
It can also be integrated with an image-to-shape module to infer 3D shape programs directly from an RGB image, leading to 3D shape reconstructions that are both more accurate and more physically plausible.

assistant: text: We propose 3D shape programs, a structured, compositional shape representation. Our model learns to infer and execute shape programs to explain 3D shapes.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Difference-Seeking Generative Adversarial Network

Paper content:
We propose a novel algorithm, Difference-Seeking Generative Adversarial Network (DSGAN), developed from traditional GAN.
DSGAN considers the scenario that the training samples of target distribution, $p_{t}$, are difficult to collect.
Suppose there are two distributions  $p_{\bar{d}}$ and $p_{d}$ such that the density of the target distribution can be the differences between the densities of $p_{\bar{d}}$ and $p_{d}$.
We show how to learn the target distribution $p_{t}$ only via samples from $p_{d}$ and $p_{\bar{d}}$ (relatively easy to obtain).
DSGAN has the flexibility to produce samples from various target distributions (e.g. the out-of-distribution).
Two key applications, semi-supervised learning and adversarial training, are taken as examples to validate the effectiveness of DSGAN.
We also provide theoretical analyses about the convergence of DSGAN.

assistant: text: This paper considers the problem of learning a GAN to capture a target distribution with only very few training samples from that distribution available.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Learning to Design RNA

Paper content:
Designing RNA molecules has garnered recent interest in medicine, synthetic biology, biotechnology and bioinformatics since many functional RNA molecules were shown to be involved in regulatory processes for transcription, epigenetics and translation.
Since an RNA's function depends on its structural properties, the RNA Design problem is to find an RNA sequence which satisfies given structural constraints.
Here, we propose a new algorithm for the RNA Design problem, dubbed LEARNA.
LEARNA uses deep reinforcement learning to train a policy network to sequentially design an entire RNA sequence given a specified target structure.
By meta-learning across 65000 different RNA Design tasks for one hour on 20 CPU cores, our extension Meta-LEARNA constructs an RNA Design policy that can be applied out of the box to solve novel RNA Design tasks.
Methodologically, for what we believe to be the first time, we jointly optimize over a rich space of architectures for the policy network, the hyperparameters of the training procedure and the formulation of the decision process.
Comprehensive empirical results on two widely-used RNA Design benchmarks, as well as a third one that we introduce, show that our approach achieves new state-of-the-art performance on the former while also being orders of magnitudes faster in reaching the previous state-of-the-art performance.
In an ablation study, we analyze the importance of our method's different components.

assistant: text: Used policy gradient optimization for generating RNA sequences which fold into a target secondary structure, resulting in clear accuracy and runtime improvements.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Promoting Coordination through Policy Regularization in Multi-Agent Deep Reinforcement Learning

Paper content:
A central challenge in multi-agent reinforcement learning is the induction of coordination between agents of a team.
In this work, we investigate how to promote inter-agent coordination using policy regularization and discuss two possible avenues respectively based on inter-agent modelling and synchronized sub-policy selection.
We test each approach in four challenging continuous control tasks with sparse rewards and compare them against three baselines including MADDPG, a state-of-the-art multi-agent reinforcement learning algorithm.
To ensure a fair comparison, we rely on a thorough hyper-parameter selection and training methodology that allows a fixed hyper-parameter search budget for each algorithm and environment.
We consequently assess both the hyper-parameter sensitivity, sample-efficiency and asymptotic performance of each learning method.
Our experiments show that the proposed methods lead to significant improvements on cooperative problems.
We further analyse the effects of the proposed regularizations on the behaviors learned by the agents.

assistant: text: This paper proposes two methods of biasing agents towards learning coordinated behaviours and evaluates both rigorously across multi-agent domains of suitable complexity.