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

Title: Hamiltonian Generative Networks

Paper content:
The Hamiltonian formalism plays a central role in classical and quantum physics.
Hamiltonians are the main tool for modelling the continuous time evolution of systems with conserved quantities, and they come equipped with many useful properties, like time reversibility and smooth interpolation in time.
These properties are important for many machine learning problems - from sequence prediction to reinforcement learning and density modelling - but are not typically provided out of the box by standard tools such as recurrent neural networks.
In this paper, we introduce the Hamiltonian Generative Network (HGN), the first approach capable of consistently learning Hamiltonian dynamics from high-dimensional observations (such as images) without restrictive domain assumptions.
Once trained, we can use HGN to sample new trajectories, perform rollouts both forward and backward in time, and even speed up or slow down the learned dynamics.
We demonstrate how a simple modification of the network architecture turns HGN into a powerful normalising flow model, called Neural Hamiltonian Flow (NHF), that uses Hamiltonian dynamics to model expressive densities.
Hence, we hope that our work serves as a first practical demonstration of the value that the Hamiltonian formalism can bring to machine learning.
More results and video evaluations are available at: http://tiny.cc/hgn

assistant: text: We introduce a class of generative models that reliably learn Hamiltonian dynamics from high-dimensional observations. The learnt Hamiltonian can be applied to sequence modeling or as a normalising flow.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Natural Language Inference over Interaction Space

Paper content:
Natural Language Inference (NLI) task requires an agent to determine the logical relationship between a natural language premise and a natural language hypothesis.
We introduce Interactive Inference Network (IIN), a novel class of neural network architectures that is able to achieve high-level understanding of the sentence pair by hierarchically extracting semantic features from interaction space.
We show that an interaction tensor (attention weight) contains semantic information to solve natural language inference, and a denser interaction tensor contains richer semantic information.
One instance of such architecture, Densely Interactive Inference Network (DIIN), demonstrates the state-of-the-art performance on large scale NLI copora and large-scale NLI alike corpus.
It's noteworthy that DIIN achieve a greater than 20% error reduction on the challenging Multi-Genre NLI (MultiNLI) dataset with respect to the strongest published system.

assistant: text: show multi-channel attention weight contains semantic feature to solve natural language inference task.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Backpropamine: training self-modifying neural networks with differentiable neuromodulated plasticity

Paper content:
The impressive lifelong learning in animal brains is primarily enabled by plastic changes in synaptic connectivity.
Importantly, these changes are not passive, but are actively controlled by neuromodulation, which is itself under the control of the brain.
The resulting self-modifying abilities of the brain play an important role in learning and adaptation, and are a major basis for biological reinforcement learning.
Here we show for the first time that artificial neural networks with such neuromodulated plasticity can be trained with gradient descent.
Extending previous work on differentiable Hebbian plasticity, we propose a differentiable formulation for the neuromodulation of plasticity.
We show that neuromodulated plasticity improves the performance of neural networks on both reinforcement learning and supervised learning tasks.
In one task, neuromodulated plastic LSTMs with millions of parameters outperform standard LSTMs on a benchmark language modeling task (controlling for the number of parameters).
We conclude that differentiable neuromodulation of plasticity offers a powerful new framework for training neural networks.

assistant: text: Neural networks can be trained to modify their own connectivity, improving their online learning performance on challenging tasks.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Weak Supervision for Time Series: Wearable Sensor Classification with Limited Labeled Data

Paper content:
Using modern deep learning models to make predictions on time series data from wearable sensors generally requires large amounts of labeled data.
However, labeling these large datasets can be both cumbersome and costly.
In this paper, we apply weak supervision to time series data, and programmatically label a dataset from sensors worn by patients with Parkinson's.
We then built a LSTM model that predicts when these patients exhibit clinically relevant freezing behavior (inability to make effective forward stepping).
We show that (1) when our model is trained using patient-specific data (prior sensor sessions), we come within 9% AUROC of a model trained using hand-labeled data and (2) when we assume no prior observations of subjects, our weakly supervised model matched performance with hand-labeled data.
These results demonstrate that weak supervision may help reduce the need to painstakingly hand label time series training data.

assistant: text: We demonstrate the feasibility of a weakly supervised time series classification approach for wearable sensor data.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Neuron as an Agent

Paper content:
Existing multi-agent reinforcement learning (MARL) communication methods have relied on a trusted third party (TTP) to distribute reward to agents, leaving them inapplicable in peer-to-peer environments.
This paper proposes reward distribution using {\em Neuron as an Agent} (NaaA) in MARL without a TTP with two key ideas:
(i) inter-agent reward distribution and
(ii) auction theory.
Auction theory is introduced because inter-agent reward distribution is insufficient for optimization.
Agents in NaaA maximize their profits (the difference between reward and cost) and, as a theoretical result, the auction mechanism is shown to have agents autonomously evaluate counterfactual returns as the values of other agents.
NaaA enables representation trades in peer-to-peer environments, ultimately regarding unit in neural networks as agents.
Finally, numerical experiments (a single-agent environment from OpenAI Gym and a multi-agent environment from ViZDoom) confirm that NaaA framework optimization leads to better performance in reinforcement learning.

assistant: text: Neuron as an Agent (NaaA) enable us to train multi-agent communication without a trusted third party.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: EXPLORING NEURAL ARCHITECTURE SEARCH FOR LANGUAGE TASKS

Paper content:
Neural architecture search (NAS), the task of finding neural architectures automatically, has recently emerged as a promising approach for unveiling better models over human-designed ones.
However, most success stories are for vision tasks and have been quite limited for text, except for a small language modeling setup.
In this paper, we explore NAS for text sequences at scale, by first focusing on the task of language translation and later extending to reading comprehension.
From a standard sequence-to-sequence models for translation, we conduct extensive searches over the recurrent cells and attention similarity functions across two translation tasks, IWSLT English-Vietnamese and WMT German-English.
We report challenges in performing cell searches as well as demonstrate initial success on attention searches with translation improvements over strong baselines.
In addition, we show that results on attention searches are transferable to reading comprehension on the SQuAD dataset.

assistant: text: We explore neural architecture search for language tasks. Recurrent cell search is challenging for NMT, but attention mechanism search works. The result of attention search on translation is transferable to reading comprehension.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Beyond Pixel Norm-Balls: Parametric Adversaries using an Analytically Differentiable Renderer

Paper content:
Many machine learning image classifiers are vulnerable to adversarial attacks, inputs with perturbations designed to intentionally trigger misclassification.
Current adversarial methods directly alter pixel colors and evaluate against pixel norm-balls: pixel perturbations smaller than a specified magnitude, according to a measurement norm.
This evaluation, however, has limited practical utility since perturbations in the pixel space do not correspond to underlying real-world phenomena of image formation that lead to them and has no security motivation attached.
Pixels in natural images are measurements of light that has interacted with the geometry of a physical scene.
As such, we propose a novel evaluation measure, parametric norm-balls, by directly perturbing physical parameters that underly image formation.
One enabling contribution we present is a physically-based differentiable renderer that allows us to propagate pixel gradients to the parametric space of lighting and geometry.
Our approach enables physically-based adversarial attacks, and our differentiable renderer leverages models from the interactive rendering literature to balance the performance and accuracy trade-offs necessary for a memory-efficient and scalable adversarial data augmentation workflow.

assistant: text: Enabled by a novel differentiable renderer, we propose a new metric that has real-world implications for evaluating adversarial machine learning algorithms, resolving the lack of realism of the existing metric based on pixel norms.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Impact of the latent space on the ability of GANs to fit the distribution

Paper content:
The goal of generative models is to model the underlying data distribution of a
sample based dataset.
Our intuition is that an accurate model should in principle
also include the sample based dataset as part of its induced probability distribution.
To investigate this, we look at fully trained generative models using the Generative
Adversarial Networks (GAN) framework and analyze the resulting generator
on its ability to memorize the dataset.
Further, we show that the size of the initial
latent space is paramount to allow for an accurate reconstruction of the training
data.
This gives us a link to compression theory, where Autoencoders (AE) are
used to lower bound the reconstruction capabilities of our generative model.
Here,
we observe similar results to the perception-distortion tradeoff (Blau & Michaeli
(2018)).
Given a small latent space, the AE produces low quality and the GAN
produces high quality outputs from a perceptual viewpoint.
In contrast, the distortion
error is smaller for the AE.
By increasing the dimensionality of the latent
space the distortion decreases for both models, but the perceptual quality only
increases for the AE.

assistant: text: We analyze the impact of the latent space of fully trained generators by pseudo inverting them.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Revisiting Self-Training for Neural Sequence Generation

Paper content:
Self-training is one of the earliest and simplest semi-supervised methods.
The key idea is to augment the original labeled dataset with unlabeled data paired with the model’s prediction.
Self-training has mostly been well-studied to classification problems.
However, in complex sequence generation tasks such as machine translation, it is still not clear how self-training woks due to the compositionality of the target space.
In this work, we first show that it is not only possible but recommended to apply self-training in sequence generation.
Through careful examination of the performance gains, we find that the noise added on the hidden states (e.g. dropout) is critical to the success of self-training, as this acts like a regularizer which forces the model to yield similar predictions for similar inputs from unlabeled data.
To further encourage this mechanism, we propose to inject noise to the input space, resulting in a “noisy” version of self-training.
Empirical study on standard benchmarks across machine translation and text summarization tasks under different resource settings shows that noisy self-training is able to effectively utilize unlabeled data and improve the baseline performance by large margin.

assistant: text: We revisit self-training as a semi-supervised learning method for neural sequence generation problem, and show that self-training can be quite successful with injected noise.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Tensor-Based Preposition Representation

Paper content:
Prepositions are among the most frequent words.
Good prepositional representation  is of great syntactic and semantic interest  in computational linguistics.
Existing methods on preposition representation either treat prepositions as content words (e.g., word2vec and GloVe) or depend heavily on external linguistic resources including syntactic parsing, training task and dataset-specific representations.
In this paper we use word-triple counts (one of the words is a preposition) to  capture the preposition's interaction with its head and children.
Prepositional  embeddings are derived via tensor decompositions on a large unlabeled corpus.
We reveal a new geometry involving Hadamard products and empirically demonstrate its utility in paraphrasing of phrasal verbs.
Furthermore, our prepositional  embeddings are used as simple features to two challenging downstream tasks: preposition selection and prepositional attachment disambiguation.
We achieve comparable to or better results than state of the art on  multiple standardized datasets.

assistant: text: This work is about tensor-based method for preposition representation training.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Bayesian Inference of Temporal Specifications to Explain How Plans Differ

Paper content:
Temporal logics are useful for describing dynamic system behavior, and have been successfully used as a language for goal definitions during task planning.
Prior works on inferring temporal logic specifications have focused on "summarizing" the input dataset -- i.e., finding specifications that are satisfied by all plan traces belonging to the given set.
In this paper, we examine the problem of inferring specifications that describe temporal differences between two sets of plan traces.
We formalize the concept of providing such contrastive explanations, then present a Bayesian probabilistic model for inferring contrastive explanations as linear temporal logic specifications.
We demonstrate the efficacy, scalability, and robustness of our model for inferring correct specifications across various benchmark planning domains and for a simulated air combat mission.

assistant: text: We present a Bayesian inference model to infer contrastive explanations (as LTL specifications) describing how two sets of plan traces differ.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Generative Models of Visually Grounded Imagination

Paper content:
It is easy for people to imagine what a man with pink hair looks like, even if they have never seen such a person before.
We call the ability to create images of novel semantic concepts visually grounded imagination.
In this paper, we show how we can modify variational auto-encoders to perform this task.
Our method uses a novel training objective, and a novel product-of-experts inference network, which can handle partially specified (abstract) concepts in a principled and efficient way.
We also propose a set of easy-to-compute evaluation metrics that capture our intuitive notions of what it means to have good visual imagination, namely correctness, coverage, and compositionality (the 3 C’s).
Finally, we perform a detailed comparison of our method with two existing joint image-attribute VAE methods (the JMVAE method of Suzuki et al., 2017 and the BiVCCA method of Wang et al., 2016) by applying them to two datasets: the MNIST-with-attributes dataset (which we introduce here), and the CelebA dataset (Liu et al., 2015).

assistant: text: A VAE-variant which can create diverse images corresponding to novel concrete or abstract "concepts" described using attribute vectors.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: On Stochastic Sign Descent Methods

Paper content:
Various gradient compression schemes have been proposed to mitigate the communication cost in distributed training of large scale machine learning models.
Sign-based methods, such as signSGD (Bernstein et al., 2018), have recently been gaining popularity because of their simple compression rule and connection to adaptive gradient methods, like ADAM.
In this paper, we perform a general analysis of sign-based methods for non-convex optimization.
Our analysis is built on intuitive bounds on success probabilities and does not rely on special noise distributions nor on the boundedness of the variance of stochastic gradients.
Extending the theory to distributed setting within a parameter server framework, we assure exponentially fast variance reduction with respect to number of nodes, maintaining 1-bit compression in both directions and using small mini-batch sizes.
We validate our theoretical findings experimentally.
One of the key factors behind the success of modern machine learning models is the availability of large amounts of training data (Bottou & Le Cun, 2003; Krizhevsky et al., 2012; Schmidhuber, 2015) .
However, the state-of-the-art deep learning models deployed in industry typically rely on datasets too large to fit the memory of a single computer, and hence the training data is typically split and stored across a number of compute nodes capable of working in parallel.
Training such models then amounts to solving optimization problems of the form
where f m : R d → R represents the non-convex loss of a deep learning model parameterized by x ∈ R d associated with data stored on node m.
Arguably, stochastic gradient descent (SGD) (Robbins & Monro, 1951; Vaswani et al., 2019; Qian et al., 2019) in of its many variants (Kingma & Ba, 2015; Duchi et al., 2011; Schmidt et al., 2017; Zeiler, 2012; Ghadimi & Lan, 2013 ) is the most popular algorithm for solving (1).
In its basic implementation, all workers m ∈ {1, 2, . . . , M } in parallel compute a random approximation g m (x k ) of ∇f m (x k ), known as the stochastic gradient.
These approximations are then sent to a master node which performs the aggregation
The aggregated vector is subsequently broadcast back to the nodes, each of which performs an update of the form x k+1 = x k − γ kĝ (x k ), thus updating their local copies of the parameters of the model.

assistant: text: General analysis of sign-based methods (e.g. signSGD) for non-convex optimization, built on intuitive bounds on success probabilities.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Demystifying Graph Neural Network Via Graph Filter Assessment

Paper content:
Graph Neural Networks (GNNs) have received tremendous attention recently due to their power in handling graph data for different downstream tasks across different application domains.
The key of GNN is its graph convolutional filters, and recently various kinds of filters are designed.
However, there still lacks in-depth analysis on (1) Whether there exists a best filter that can perform best on all graph data; (2) Which graph properties will influence the optimal choice of graph filter; (3) How to design appropriate filter adaptive to the graph data.
In this paper, we focus on addressing the above three questions.
We first propose a novel assessment tool to evaluate the effectiveness of graph convolutional filters for a given graph.
Using the assessment tool, we find out that there is no single filter as a `silver bullet' that perform the best on all possible graphs.
In addition, different graph structure properties will influence the optimal graph convolutional filter's design choice.
Based on these findings, we develop Adaptive Filter Graph Neural Network (AFGNN), a simple but powerful model that can adaptively learn task-specific filter.
For a given graph, it leverages graph filter assessment as regularization and learns to combine from a set of base filters.
Experiments on both synthetic and real-world benchmark datasets demonstrate that our proposed model can indeed learn an appropriate filter and perform well on graph tasks.

assistant: text: Propose an assessment framework to analyze and learn graph convolutional filter
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Small nonlinearities in activation functions create bad local minima in neural networks

Paper content:
We investigate the loss surface of neural networks.
We prove that even for one-hidden-layer networks with "slightest" nonlinearity, the empirical risks have spurious local minima in most cases.
Our results thus indicate that in general "no spurious local minim" is a property limited to deep linear networks, and insights obtained from linear networks may not be robust.
Specifically, for ReLU(-like) networks we constructively prove that for almost all practical datasets there exist infinitely many local minima.
We also present a counterexample for more general activations (sigmoid, tanh, arctan, ReLU, etc.), for which there exists a bad local minimum.
Our results make the least restrictive assumptions relative to existing results on spurious local optima in neural networks.
We complete our discussion by presenting a comprehensive characterization of global optimality for deep linear networks, which unifies other results on this topic.

assistant: text: We constructively prove that even the slightest nonlinear activation functions introduce spurious local minima, for general datasets and activation functions.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: All Neural Networks are Created Equal

Paper content:
One of the unresolved questions in deep learning is the nature of the solutions that are being discovered.
We investigate the collection of solutions reached by the same network architecture, with different random initialization of weights and random mini-batches.
These solutions are shown to be rather similar - more often than not, each train and test example is either classified correctly by all the networks, or by none at all.
Surprisingly, all the network instances seem to share the same learning dynamics, whereby initially the same train and test examples are correctly recognized by the learned model, followed by other examples which are learned in roughly the same order.
When extending the investigation to heterogeneous collections of neural network architectures, once again examples are seen to be learned in the same order irrespective of architecture, although the more powerful architecture may continue to learn and thus achieve higher accuracy.
This pattern of results remains true even when the composition of classes in the test set is unrelated to the train set, for example, when using out of sample natural images or even artificial images.
To show the robustness of these phenomena we provide an extensive summary of our empirical study, which includes hundreds of graphs describing tens of thousands of networks with varying NN architectures, hyper-parameters and domains.
We also discuss cases where this pattern of similarity breaks down, which show that the reported similarity is not an artifact of optimization by gradient descent.
Rather, the observed pattern of similarity is characteristic of learning complex problems with big networks.
Finally, we show that this pattern of similarity seems to be strongly correlated with effective generalization.

assistant: text: Most neural networks approximate the same classification function, even across architectures, through all stages of learning.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: A General Upper Bound for Unsupervised Domain Adaptation

Paper content:
In this work, we present a novel upper bound of target error to address the problem for unsupervised domain adaptation.
Recent studies reveal that a deep neural network can learn transferable features which generalize well to novel tasks.
Furthermore,  Ben-David et al. (2010) provide an upper bound for target error when transferring the knowledge, which can be summarized as minimizing the source error and  distance between marginal distributions simultaneously.
However, common methods based on the theory usually ignore the joint error such that samples from different classes might be mixed together when matching marginal distribution.
And in such case, no matter how we minimize the marginal discrepancy, the target error is not bounded due to an increasing joint error.
To address this problem, we propose a general upper bound taking joint error into account, such that the undesirable case can be properly penalized.
In addition, we utilize constrained hypothesis space to further formalize a tighter bound as well as a novel cross margin discrepancy to measure the dissimilarity between hypotheses which alleviates instability during adversarial learning.
Extensive empirical evidence shows that our proposal outperforms related approaches in image classification error rates on standard domain adaptation benchmarks.

assistant: text: joint error matters for unsupervised domain adaptation especially when the domain shift is huge
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Discrete Wasserstein Generative Adversarial Networks (DWGAN)

Paper content:
Generating complex discrete distributions remains as one of the challenging problems in machine learning.
Existing techniques for generating complex distributions with high degrees of freedom depend on standard generative models like Generative Adversarial Networks (GAN), Wasserstein GAN, and associated variations.
Such models are based on an optimization involving the distance between two continuous distributions.
We introduce a Discrete Wasserstein GAN (DWGAN) model which is based on a dual formulation of the Wasserstein distance between two discrete distributions.
We derive a novel training algorithm and corresponding network architecture based on the formulation.
Experimental results are provided for both synthetic discrete data, and real discretized data from MNIST handwritten digits.

assistant: text: We propose a Discrete Wasserstein GAN (DWGAN) model which is based on a dual formulation of the Wasserstein distance between two discrete distributions.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Data-Efficient Image Recognition with Contrastive Predictive Coding

Paper content:
Human observers can learn to recognize new categories of objects from a handful of examples, yet doing so with machine perception remains an open challenge.
We hypothesize that data-efficient recognition is enabled by representations which make the variability in natural signals more predictable, as suggested by recent perceptual evidence.
We therefore revisit and improve Contrastive Predictive Coding, a recently-proposed unsupervised learning framework, and arrive at a representation which enables generalization from small amounts of labeled data.
When provided with only 1% of ImageNet labels (i.e. 13 per class), this model retains a strong classification performance, 73% Top-5 accuracy, outperforming supervised networks by 28% (a 65% relative improvement) and state-of-the-art semi-supervised methods by 14%.
We also find this representation to serve as a useful substrate for object detection on the PASCAL-VOC 2007 dataset, approaching the performance of representations trained with a fully annotated ImageNet dataset.

assistant: text: Unsupervised representations learned with Contrastive Predictive Coding enable data-efficient image classification.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Regularizing Trajectories to Mitigate Catastrophic Forgetting

Paper content:
Regularization-based continual learning approaches generally prevent catastrophic forgetting by augmenting the training loss with an auxiliary objective.
However in most practical optimization scenarios with noisy data and/or gradients, it is possible that stochastic gradient descent can inadvertently change critical parameters.
In this paper, we argue for the importance of regularizing optimization trajectories directly.
We derive a new co-natural gradient update rule for continual learning whereby the new task gradients are preconditioned with the empirical Fisher information of previously learnt tasks.
We show that using the co-natural gradient systematically reduces forgetting in continual learning.
Moreover, it helps combat overfitting when learning a new task in a low resource scenario.
It is good to have an end to journey toward; but it is the journey that matters, in the end.
We have presented the co-natural gradient, a technique that regularizes the optimization trajectory of models trained in a continual setting.
We have shown that the co-natural gradient stands on its own as an efficient approach for overcoming catastrophic forgetting, and that it effectively complements and stabilizes other existing techniques at a minimal cost.
We believe that the co-natural gradientand more generally, trajectory regularization -can serve as a solid bedrock for building agents that learn without forgetting.

assistant: text: Regularizing the optimization trajectory with the Fisher information of old tasks reduces catastrophic forgetting greatly
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Regularizing and Optimizing LSTM Language Models

Paper content:
In this paper, we consider the specific problem of word-level language modeling and investigate strategies for regularizing and optimizing LSTM-based models.
We propose the weight-dropped LSTM, which uses DropConnect on hidden-to-hidden weights, as a form of recurrent regularization.
Further, we introduce NT-ASGD, a non-monotonically triggered  (NT) variant of the averaged stochastic gradient method (ASGD), wherein the averaging trigger is determined using a NT condition as opposed to being tuned by the user.
Using these and other regularization strategies, our ASGD Weight-Dropped LSTM (AWD-LSTM) achieves state-of-the-art word level perplexities on two data sets: 57.3 on Penn Treebank and 65.8 on WikiText-2.
In exploring the effectiveness of a neural cache in conjunction with our proposed model, we achieve an even lower state-of-the-art perplexity of 52.8 on Penn Treebank and 52.0 on WikiText-2.
We also explore the viability of the proposed regularization and optimization strategies in the context of the quasi-recurrent neural network (QRNN) and demonstrate comparable performance to the AWD-LSTM counterpart.
The code for reproducing the results is open sourced and is available at https://github.com/salesforce/awd-lstm-lm.

assistant: text: Effective regularization and optimization strategies for LSTM-based language models achieves SOTA on PTB and WT2.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: A Simple Neural Attentive Meta-Learner

Paper content:
Deep neural networks excel in regimes with large amounts of data, but tend to struggle when data is scarce or when they need to adapt quickly to changes in the task.
In response, recent work in meta-learning proposes training a meta-learner on a distribution of similar tasks, in the hopes of generalization to novel but related tasks by learning a high-level strategy that captures the essence of the problem it is asked to solve.
However, many recent meta-learning approaches are extensively hand-designed, either using architectures specialized to a particular application, or hard-coding algorithmic components that constrain how the meta-learner solves the task.
We propose a class of simple and generic meta-learner architectures that use a novel combination of temporal convolutions and soft attention; the former to aggregate information from past experience and the latter to pinpoint specific pieces of information.
In the most extensive set of meta-learning experiments to date, we evaluate the resulting Simple Neural AttentIve Learner (or SNAIL) on several heavily-benchmarked tasks.
On all tasks, in both supervised and reinforcement learning, SNAIL attains state-of-the-art performance by significant margins.

assistant: text: a simple RNN-based meta-learner that achieves SOTA performance on popular benchmarks
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Compositional GAN: Learning Conditional Image Composition

Paper content:
Generative Adversarial Networks (GANs) can produce images of surprising complexity and realism, but are generally structured to sample from a single latent source ignoring the explicit spatial interaction between multiple entities that could be present in a scene.
Capturing such complex interactions between different objects in the world, including their relative scaling, spatial layout, occlusion, or viewpoint transformation is a challenging problem.
In this work, we propose to model object composition in a GAN framework as a self-consistent composition-decomposition network.
Our model is conditioned on the object images from their marginal distributions and can generate a realistic image from their joint distribution.
We evaluate our model through qualitative experiments and user evaluations in scenarios when either paired or unpaired examples for the individual object images and the joint scenes are given during training.
Our results reveal that the learned model captures potential interactions between the two object domains given as input to output new instances of composed scene at test time in a reasonable fashion.

assistant: text: We develop a novel approach to model object compositionality in images in a GAN framework.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Directed-Info GAIL: Learning Hierarchical Policies from Unsegmented Demonstrations using Directed Information

Paper content:
The use of imitation learning to learn a single policy for a complex task that has multiple modes or hierarchical structure can be challenging.
In fact, previous work has shown that when the modes are known, learning separate policies for each mode or sub-task can greatly improve the performance of imitation learning.
In this work, we discover the interaction between sub-tasks from their resulting state-action trajectory sequences using a directed graphical model.
We propose a new algorithm based on the generative adversarial imitation learning framework which automatically learns sub-task policies from unsegmented demonstrations.
Our approach maximizes the directed information flow in the graphical model between sub-task latent variables and their generated trajectories.
We also show how our approach connects with the existing Options framework, which is commonly used to learn hierarchical policies.

assistant: text: Learning Hierarchical Policies from Unsegmented Demonstrations using Directed Information
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: SuperTML: Two-Dimensional Word Embedding and Transfer Learning Using ImageNet Pretrained CNN Models for the Classifications on Tabular Data

Paper content:
Structured tabular data is the most commonly used form of data in industry according to a Kaggle ML and DS Survey.
Gradient Boosting Trees, Support Vector Machine, Random Forest, and Logistic Regression are typically used for classification tasks on tabular data.
The recent work of Super Characters method using two-dimensional word embedding achieved state-of-the-art results in text classification tasks, showcasing the promise of this new approach.
In this paper, we propose the SuperTML method, which borrows the idea of Super Characters method and two-dimensional embedding to address the problem of classification on tabular data.
For each input of tabular data, the features are first projected into two-dimensional embedding like an image, and then this image is fed into fine-tuned ImageNet CNN models for classification.
Experimental results have shown that the proposed SuperTML method have achieved state-of-the-art results on both large and small datasets.

assistant: text: Deep learning for structured tabular data machine learning using pre-trained CNN model from ImageNet.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: MetaPoison:   Learning to craft adversarial poisoning examples via meta-learning

Paper content:
We consider a new class of \emph{data poisoning} attacks on neural networks, in which the attacker takes control of a model by making small perturbations to a subset of its training data.
We formulate the task of finding poisons as a bi-level optimization problem, which can be solved using methods borrowed from the meta-learning community.
Unlike previous poisoning strategies, the meta-poisoning can poison networks that are trained from scratch using an initialization unknown to the attacker and transfer across hyperparameters.
Further we show that our attacks are more versatile: they can cause misclassification of the target image into an arbitrarily chosen class.
Our results show above 50% attack success rate when poisoning just 3-10% of the training dataset.
We have extended learning-to-learn techniques to adversarial poison example generation, or learning-to-craft.
We devised a novel fast algorithm by which to solve the bi-level optimization inherent to poisoning, where the inner training of the network on the perturbed dataset must be performed for every crafting step.
Our results, showing the first clean-label poisoning attack that works on networks trained from scratch, demonstrates the effectiveness of this method.
Further our attacks are versatile, they have functionality such as the third-party attack which are not possible with previous methods.
We hope that our work establishes a strong attack baseline for future work on clean-label data poisoning and also promote caution that these new methods of data poisoning are able to muster a strong attack on industrially-relevant architectures, that even transfers between training runs and hyperparameter choices.

assistant: text: Generate corrupted training images that are imperceptible yet change CNN behavior on a target during any new training.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Network Signatures from Image Representation of Adjacency Matrices: Deep/Transfer Learning for Subgraph Classification

Paper content:
We propose a novel subgraph image representation for classification of network fragments with the target being their parent networks.
The graph image representation is based on 2D image embeddings of adjacency matrices.
We use this image representation in two modes.
First, as the input to a machine learning algorithm.
Second, as the input to a pure transfer learner.
Our conclusions from multiple datasets are that
1. deep learning using structured image features performs the best compared to graph kernel and classical features based methods; and,
2. pure transfer learning works effectively with minimum interference from the user and is robust against small data.
Our experiments overwhelmingly show that the structured image representation of graphs achieves successful graph classification with ease.
The image representation is lossless, that is the image embeddings contain all the information in the corresponding adjacency matrix.
Our results also show that even with very little information about the parent network, Deep network models are able to extract network signatures.
Specifically, with just 64-node samples from networks with up to 1 million nodes, we were able to predict the parent network with > 90% accuracy while being significantly better than random with only 8-node samples.
Further, we demonstrated that the image embedding approach provides many advantages over graph kernel and feature-based methods.We also presented an approach to graph classification using transfer learning from a completely different domain.
Our approach converts graphs into 2D image embeddings and uses a pre-trained image classifier (Caffe) to obtain label-vectors.
In a range of experiments with real-world data sets, we have obtained accuracies from 70% to 94% for 2-way classification and 61% for multi-way classification.
Further, our approach is highly resilient to training-to-test ratio, that is, can work with sparse training samples.
Our results show that such an approach is very promising, especially for applications where training data is not readily available (e.g. terrorist networks).Future
work includes improvements to the transfer learning by improving the distance function between label-vectors, as well as using the probabilities from Caffe. Further
, we would also look to generalize this approach to other domains, for example classifying radio frequency map samples using transfer learning.

assistant: text: We convert subgraphs into structured images and classify them using 1. deep learning and 2. transfer learning (Caffe) and achieve stunning results.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Modeling treatment events in disease progression

Paper content:
Ability to quantify and predict progression of a disease is fundamental for selecting an appropriate treatment.
Many clinical metrics cannot be acquired frequently either because of their cost (e.g. MRI, gait analysis) or because they are inconvenient or harmful to a patient (e.g. biopsy, x-ray).
In such scenarios, in order to estimate individual trajectories of disease progression, it is advantageous to leverage similarities between patients, i.e. the covariance of trajectories, and find a latent representation of progression.
Most of existing methods for estimating trajectories do not account for events in-between observations, what dramatically decreases their adequacy for clinical practice.
In this study, we develop a machine learning framework named Coordinatewise-Soft-Impute (CSI) for analyzing disease progression from sparse observations in the presence of confounding events.
CSI is guaranteed to converge to the global minimum of the corresponding optimization problem.
Experimental results also demonstrates the effectiveness of CSI using both simulated and real dataset.

assistant: text: A novel matrix completion based algorithm to model disease progression with events
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: RTFM: Generalising to New Environment Dynamics via Reading

Paper content:
Obtaining policies that can generalise to new environments in reinforcement learning is challenging.
In this work, we demonstrate that language understanding via a reading policy learner is a promising vehicle for generalisation to new environments.
We propose a grounded policy learning problem, Read to Fight Monsters (RTFM), in which the agent must jointly reason over a language goal, relevant dynamics described in a document, and environment observations.
We procedurally generate environment dynamics and corresponding language descriptions of the dynamics, such that agents must read to understand new environment dynamics instead of memorising any particular information.
In addition, we propose txt2π, a model that captures three-way interactions between the goal, document, and observations.
On RTFM, txt2π generalises to new environments with dynamics not seen during training via reading.
Furthermore, our model outperforms baselines such as FiLM and language-conditioned CNNs on RTFM.
Through curriculum learning, txt2π produces policies that excel on complex RTFM tasks requiring several reasoning and coreference steps.

assistant: text: We show language understanding via reading is promising way to learn policies that generalise to new environments.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Excessive Invariance Causes Adversarial Vulnerability

Paper content:
Despite their impressive performance, deep neural networks exhibit striking failures on out-of-distribution inputs.
One core idea of adversarial example research is to reveal neural network errors under such distribution shifts.
We decompose these errors into two complementary sources: sensitivity and invariance.
We show deep networks are not only too sensitive to task-irrelevant changes of their input, as is well-known from epsilon-adversarial examples, but are also too invariant to a wide range of task-relevant changes, thus making vast regions in input space vulnerable to adversarial attacks.
We show such excessive invariance occurs across various tasks and architecture types.
On MNIST and ImageNet one can manipulate the class-specific content of almost any image without changing the hidden activations.
We identify an insufficiency of the standard cross-entropy loss as a reason for these failures.
Further, we extend this objective based on an information-theoretic analysis so it encourages the model to consider all task-dependent features in its decision.
This provides the first approach tailored explicitly to overcome excessive invariance and resulting vulnerabilities.

assistant: text: We show deep networks are not only too sensitive to task-irrelevant changes of their input, but also too invariant to a wide range of task-relevant changes, thus making vast regions in input space vulnerable to adversarial attacks.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: WRPN: Wide Reduced-Precision Networks

Paper content:
For computer vision applications, prior works have shown the efficacy of reducing numeric precision of model parameters (network weights) in deep neural networks.
Activation maps, however, occupy a large memory footprint during both the training and inference step when using mini-batches of inputs.
One way to reduce this large memory footprint is to reduce the precision of activations.
However, past works have shown that reducing the precision of activations hurts model accuracy.
We study schemes to train networks from scratch using reduced-precision activations without hurting accuracy.
We reduce the precision of activation maps (along with model parameters) and increase the number of filter maps in a layer, and find that this scheme matches or surpasses the accuracy of the baseline full-precision network.
As a result, one can significantly improve the execution efficiency (e.g. reduce dynamic memory footprint, memory band- width and computational energy) and speed up the training and inference process with appropriate hardware support.
We call our scheme WRPN -- wide reduced-precision networks.
We report results and show that WRPN scheme is better than previously reported accuracies on ILSVRC-12 dataset while being computationally less expensive compared to previously reported reduced-precision networks.

assistant: text: Lowering precision (to 4-bits, 2-bits and even binary) and widening the filter banks gives as accurate network as those obtained with FP32 weights and activations.