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

Title: Enhancing the Transferability of Adversarial Examples with Noise Reduced Gradient

Paper content:
Deep neural networks provide state-of-the-art performance for many applications of interest.
Unfortunately they are known to be vulnerable to adversarial examples, formed by applying small but malicious perturbations to the original inputs.
Moreover, the perturbations can transfer across models: adversarial examples generated for a specific model will often mislead other unseen models.
Consequently  the adversary can leverage it to attack against the deployed black-box systems.
In this work, we demonstrate that the adversarial perturbation can be decomposed into two components: model-specific and data-dependent one, and it is the latter that mainly contributes to the transferability.
Motivated by this understanding, we propose to craft adversarial examples by utilizing the noise reduced gradient (NRG) which approximates the data-dependent component.
Experiments on various classification models trained on ImageNet demonstrates that the new approach enhances the transferability dramatically.
We also find that low-capacity models have more powerful attack capability than high-capacity counterparts, under the condition that they have comparable test performance.
These insights give rise to a principled manner to construct adversarial examples with high success rates and could potentially provide us guidance for designing effective defense approaches against black-box attacks.

assistant: text: This paper focuses on enhancing the transferability of adversarial examples from one model to another model.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Anomaly Detection with Generative Adversarial Networks

Paper content:
Many anomaly detection methods exist that perform well on low-dimensional problems however there is a notable lack of effective methods for high-dimensional spaces, such as images.
Inspired by recent successes in deep learning we propose a novel approach to anomaly detection using generative adversarial networks.
Given a sample under consideration, our method is based on searching for a good representation of that sample in the latent space of the generator; if such a representation is not found, the sample is deemed anomalous.
We achieve state-of-the-art performance on standard image benchmark datasets and visual inspection of the most anomalous samples reveals that our method does indeed return anomalies.

assistant: text: A GAN based approach to doing anomaly detection for image data where the generator's latent space is explored to find a representation for a test image.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Graph2Seq: Graph to Sequence Learning with Attention-Based Neural Networks

Paper content:
The celebrated Sequence to Sequence learning (Seq2Seq) technique and its numerous variants achieve excellent performance on many tasks.
However, many machine learning tasks have inputs naturally represented as graphs; existing Seq2Seq models face a significant challenge in achieving accurate conversion from graph form to the appropriate sequence.
To address this challenge, we introduce a general end-to-end graph-to-sequence neural encoder-decoder architecture that maps an input graph to a sequence of vectors and uses an attention-based LSTM method to decode the target sequence from these vectors.
Our method first generates the node and graph embeddings using an improved graph-based neural network with a novel aggregation strategy to incorporate edge direction information in the node embeddings.
We further introduce an attention mechanism that aligns node embeddings and the decoding sequence to better cope with large graphs.
Experimental results on bAbI, Shortest Path, and Natural Language Generation tasks demonstrate that our model achieves state-of-the-art performance and significantly outperforms existing graph neural networks, Seq2Seq, and Tree2Seq models; using the proposed bi-directional node embedding aggregation strategy, the model can converge rapidly to the optimal performance.

assistant: text: A graph2seq architecture that combines a graph encoder mixing GGNN and GCN components with an attentional sequence encoder, and that shows improvements over baselines.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Scale-Equivariant Neural Networks with Decomposed Convolutional Filters

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

assistant: text: The authors propose a CNN architecture that is theoretically equivariant to isotropic scalings and translations by adding an extra scale-dimension to activation tensors.
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 proposes three Discrete Transformers: a discrete and stochastic Gumbel-softmax based attention module, a two-stream syntactic and semantic transformer, and sparsity regularization.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Ternary MobileNets via Per-Layer Hybrid Filter Banks

Paper content:
MobileNets family of computer vision neural networks have fueled tremendous progress in the design and organization of resource-efficient architectures in recent years.
New applications with stringent real-time requirements in highly constrained devices require further compression of MobileNets-like already computeefficient networks.
Model quantization is a widely used technique to compress and accelerate neural network inference and prior works have quantized MobileNets to 4 − 6 bits albeit with a modest to significant drop in accuracy.
While quantization to sub-byte values (i.e. precision ≤ 8 bits) has been valuable, even further quantization of MobileNets to binary or ternary values is necessary to realize significant energy savings and possibly runtime speedups on specialized hardware, such as ASICs and FPGAs.
Under the key observation that convolutional filters at each layer of a deep neural network may respond differently to ternary quantization, we propose a novel quantization method that generates per-layer hybrid filter banks consisting of full-precision and ternary weight filters for MobileNets.
The layer-wise hybrid filter banks essentially combine the strengths of full-precision and ternary weight filters to derive a compact, energy-efficient architecture for MobileNets.
Using this proposed quantization method, we quantized a substantial portion of weight filters of MobileNets to ternary values resulting in 27.98% savings in energy, and a 51.07% reduction in the model size, while achieving comparable accuracy and no degradation in throughput on specialized hardware in comparison to the baseline full-precision MobileNets.

assistant: text: 2x savings in model size, 28% energy reduction for MobileNets on ImageNet at no loss in accuracy using hybrid layers composed of conventional full-precision filters and ternary filters
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Gradient Descent Provably Optimizes Over-parameterized Neural Networks

Paper content:
One of the mysteries in the success of neural networks is randomly initialized first order methods like gradient descent can achieve zero training loss even though the objective function is non-convex and non-smooth.
This paper demystifies this surprising phenomenon for two-layer fully connected ReLU activated neural networks.
For an $m$ hidden node shallow neural network with ReLU activation and $n$ training data, we show as long as $m$ is large enough and no two inputs are parallel, randomly initialized gradient descent converges to a globally optimal solution at a linear convergence rate for the quadratic loss function.
Our analysis relies on the following observation: over-parameterization and random initialization jointly restrict every weight vector to be close to its initialization for all iterations, which allows us to exploit a strong convexity-like property to show that gradient descent converges at a global linear rate to the global optimum.
We believe these insights are also useful in analyzing deep models and other first order methods.

assistant: text: This work considers optimizing a two-layer over-parameterized ReLU network with the squared loss and given a data set with arbituary labels.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: GUIDEGAN:  ATTENTION  BASED  SPATIAL  GUIDANCE FOR  IMAGE-TO-IMAGE TRANSLATION

Paper content:
Recently, Generative Adversarial Network (GAN) and numbers of its variants have been widely used to solve the image-to-image translation problem and achieved extraordinary results in both a supervised and unsupervised manner.
However, most GAN-based methods suffer from the imbalance problem between the generator and discriminator in practice.
Namely, the relative model capacities of the generator and discriminator do not match, leading to mode collapse and/or diminished gradients.
To tackle this problem, we propose a GuideGAN based on attention mechanism.
More specifically, we arm the discriminator with an attention mechanism so not only it estimates the probability that its input is real, but also does it create an attention map that highlights the critical features for such prediction.
This attention map then assists the generator to produce more plausible and realistic images.
We extensively evaluate the proposed GuideGAN framework on a  number of image transfer tasks.
Both qualitative results and quantitative comparison demonstrate the superiority of our proposed approach.

assistant: text: Proposal for a GAN with an attention-based discriminator for I2I translation which provides the probability of real/fake and an attention map which reflects salience for image generation.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Building Hierarchical Interpretations in Natural Language via Feature Interaction Detection

Paper content:
The interpretability of neural networks has become crucial for their applications in real world with respect to the reliability and trustworthiness.
Existing explanation generation methods usually provide important features by scoring their individual contributions to the model prediction and ignore the interactions between features, which eventually provide a bag-of-words representation as explanation.
In natural language processing, this type of explanations is challenging for human user to understand the meaning of an explanation and draw the connection between explanation and model prediction, especially for long texts.
In this work, we focus on detecting the interactions between features, and propose a novel approach to build a hierarchy of explanations based on feature interactions.
The proposed method is evaluated with three neural classifiers, LSTM, CNN, and BERT, on two benchmark text classification datasets.
The generated explanations are assessed by both automatic evaluation measurements and human evaluators.
Experiments show the effectiveness of the proposed method in providing explanations that are both faithful to models, and understandable to humans.

assistant: text: A novel approach to construct hierarchical explanations for text classification by detecting feature interactions.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: AutoQ: Automated Kernel-Wise Neural Network Quantization 

Paper content:
Network quantization is one of the most hardware friendly techniques to enable the deployment of convolutional neural networks (CNNs) on low-power mobile devices.
Recent network quantization techniques quantize each weight kernel in a convolutional layer independently for higher inference accuracy, since the weight kernels in a layer exhibit different variances and hence have different amounts of redundancy.
The quantization bitwidth or bit number (QBN) directly decides the inference accuracy, latency, energy and hardware overhead.
To effectively reduce the redundancy and accelerate CNN inferences, various weight kernels should be quantized with different QBNs.
However, prior works use only one QBN to quantize each convolutional layer or the entire CNN, because the design space of searching a QBN for each weight kernel is too large.
The hand-crafted heuristic of the kernel-wise QBN search is so sophisticated that domain experts can obtain only sub-optimal results.
It is difficult for even deep reinforcement learning (DRL) DDPG-based agents to find a kernel-wise QBN configuration that can achieve reasonable inference accuracy.
In this paper, we propose a hierarchical-DRL-based kernel-wise network quantization technique, AutoQ, to automatically search a QBN for each weight kernel, and choose another QBN for each activation layer.
Compared to the models quantized by the state-of-the-art DRL-based schemes, on average, the same models quantized by AutoQ reduce the inference latency by 54.06%, and decrease the inference energy consumption by 50.69%, while achieving the same inference accuracy.

assistant: text: A method for quantizing neural network weights and activations that uses deep reinforcement learning to select bitwidth for individual kernels in a layer and that achieves better performance, or latency, than prior approaches.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Exemplar Guided Unsupervised Image-to-Image Translation with Semantic Consistency

Paper content:
Image-to-image translation has recently received significant attention due to advances in deep learning.
Most works focus on learning either a one-to-one mapping in an unsupervised way or a many-to-many mapping in a supervised way.
However, a more practical setting is many-to-many mapping in an unsupervised way, which is harder due to the lack of supervision and the complex inner- and cross-domain variations.
To alleviate these issues, we propose the Exemplar Guided & Semantically Consistent Image-to-image Translation (EGSC-IT) network which conditions the translation process on an exemplar image in the target domain.
We assume that an image comprises of a content component which is shared across domains, and a style component specific to each domain.
Under the guidance of an exemplar from the target domain we apply Adaptive Instance Normalization to the shared content component, which allows us to transfer the style information of the target domain to the source domain.
To avoid semantic inconsistencies during translation that naturally appear due to the large inner- and cross-domain variations, we introduce the concept of feature masks that provide coarse semantic guidance without requiring the use of any semantic labels.
Experimental results on various datasets show that EGSC-IT does not only translate the source image to diverse instances in the target domain, but also preserves the semantic consistency during the process.

assistant: text: Discusses a core failing and need for I2I translation models.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Grouping-By-ID: Guarding Against Adversarial Domain Shifts

Paper content:
When training a deep neural network for supervised image classification, one can broadly distinguish between two types of latent features of images that will drive the classification of class Y. Following the notation of Gong et al. (2016), we can divide features broadly into the classes of
(i) “core” or “conditionally invariant” features X^ci whose distribution P(X^ci | Y) does not change substantially across domains and
(ii) “style” or “orthogonal” features X^orth whose distribution P(X^orth | Y) can change substantially across domains.
These latter orthogonal features would generally include features such as position, rotation, image quality or brightness but also more complex ones like hair color or posture for images of persons.
We try to guard against future adversarial domain shifts by ideally just using the “conditionally invariant” features for classification.
In contrast to previous work, we assume that the domain itself is not observed and hence a latent variable.
We can hence not directly see the distributional change of features across different domains.
We do assume, however, that we can sometimes observe a so-called identifier or ID variable.
We might know, for example, that two images show the same person, with ID referring to the identity of the person.
In data augmentation, we generate several images from the same original image, with ID referring to the relevant original image.
The method requires only a small fraction of images to have an ID variable.
We provide a causal framework for the problem by adding the ID variable to the model of Gong et al. (2016).
However, we are interested in settings where we cannot observe the domain directly and we treat domain as a latent variable.
If two or more samples share the same class and identifier, (Y, ID)=(y,i), then we treat those samples as counterfactuals under different style interventions on the orthogonal or style features.
Using this grouping-by-ID approach, we regularize the network to provide near constant output across samples that share the same ID by penalizing with an appropriate graph Laplacian.
This is shown to substantially improve performance in settings where domains change in terms of image quality, brightness, color changes, and more complex changes such as changes in movement and posture.
We show links to questions of interpretability, fairness and transfer learning.

assistant: text: We propose counterfactual regularization to guard against adversarial domain shifts arising through shifts in the distribution of latent "style features" of images.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Uncertainty in Multitask Transfer Learning

Paper content:
Using variational Bayes neural networks, we develop an algorithm capable of accumulating knowledge into a prior from multiple different tasks.
This results in a rich prior capable of few-shot learning on new tasks.
The posterior can go beyond the mean field approximation and yields good uncertainty on the performed experiments.
Analysis on toy tasks show that it can learn from significantly different tasks while finding similarities among them.
Experiments on Mini-Imagenet reach state of the art with 74.5% accuracy on 5 shot learning.
Finally, we provide two new benchmarks, each showing a failure mode of existing meta learning algorithms such as MAML and prototypical Networks.

assistant: text: The paper presents a method for training a probabilistic model for Multitasks Transfer Learning by introducing a latent variable per task to capture the commonality in the task instances.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Spectral Inference Networks: Unifying Deep and Spectral Learning

Paper content:
We present Spectral Inference Networks, a framework for learning eigenfunctions of linear operators by stochastic optimization.
Spectral Inference Networks generalize Slow Feature Analysis to generic symmetric operators, and are closely related to Variational Monte Carlo methods from computational physics.
As such, they can be a powerful tool for unsupervised representation learning from video or graph-structured data.
We cast training Spectral Inference Networks as a bilevel optimization problem, which allows for online learning of multiple eigenfunctions.
We show results of training Spectral Inference Networks on problems in quantum mechanics and feature learning for videos on synthetic datasets.
Our results demonstrate that Spectral Inference Networks accurately recover eigenfunctions of linear operators and can discover interpretable representations from video in a fully unsupervised manner.

assistant: text: We show how to learn spectral decompositions of linear operators with deep learning, and use it for unsupervised learning without a generative model.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Neural Markov Logic Networks

Paper content:
We introduce Neural Markov Logic Networks (NMLNs), a statistical relational learning system that borrows ideas from Markov logic.
Like Markov Logic Networks (MLNs), NMLNs are an exponential-family model for modelling distributions over possible worlds, but unlike MLNs, they do not rely on explicitly specified first-order logic rules.
Instead, NMLNs learn an implicit representation of such rules as a neural network that acts as a potential function on fragments of the relational structure.
Interestingly, any MLN can be represented as an NMLN.
Similarly to recently proposed Neural theorem provers (NTPs) (Rocktaschel at al. 2017), NMLNs can exploit embeddings of constants but, unlike NTPs, NMLNs work well also in their absence.
This is extremely important for predicting in settings other than the transductive one.
We showcase the potential of NMLNs on knowledge-base completion tasks and on generation of molecular (graph) data.

assistant: text: We introduce a statistical relational learning system that borrows ideas from Markov logic but learns an implicit representation of rules as a neural network.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: On Incorporating Semantic Prior Knowlegde in Deep Learning Through Embedding-Space Constraints

Paper content:
The knowledge that humans hold about a problem often extends far beyond a set of training data and output labels.
While the success of deep learning mostly relies on supervised training, important properties cannot be inferred efficiently from end-to-end annotations alone, for example causal relations or domain-specific invariances.
We present a general technique to supplement supervised training with prior knowledge expressed as relations between training instances.
We illustrate the method on the task of visual question answering to exploit various auxiliary annotations, including relations of equivalence and of logical entailment between questions.
Existing methods to use these annotations, including auxiliary losses and data augmentation, cannot guarantee the strict inclusion of these relations into the model since they require a careful balancing against the end-to-end objective.
Our method uses these relations to shape the embedding space of the model, and treats them as strict constraints on its learned representations.
%The resulting model encodes relations that better generalize across instances.
In the context of VQA, this approach brings significant improvements in accuracy and robustness, in particular over the common practice of incorporating the constraints as a soft regularizer.
We also show that incorporating this type of prior knowledge with our method brings consistent improvements, independently from the amount of supervised data used.
It demonstrates the value of an additional training signal that is otherwise difficult to extract from end-to-end annotations alone.

assistant: text: The paper argues for encoding external knowledge in the linguistic embedding layer of a multimodal neural network, as a set of hard constraints.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Long Short-Term Memory as a Dynamically Computed Element-wise Weighted Sum

Paper content:
Long short-term memory networks (LSTMs) were introduced to combat vanishing gradients in simple recurrent neural networks (S-RNNs) by augmenting them with additive recurrent connections controlled by gates.
We present an alternate view to explain the success of LSTMs: the gates themselves are powerful recurrent models that provide more representational power than previously appreciated.
We do this by showing that the LSTM's gates can be decoupled from the embedded S-RNN, producing a restricted class of RNNs where the main recurrence computes an element-wise weighted sum of context-independent functions of the inputs.
Experiments on a range of challenging NLP problems demonstrate that the simplified gate-based models work substantially better than S-RNNs, and often just as well as the original LSTMs, strongly suggesting that the gates are doing much more in practice than just alleviating vanishing gradients.

assistant: text: The paper proposes a new insight to LSTM in which the core is an element-wise weighted sum and argues that LSTM is redundant by keeping only input and forget gates to compute the weights
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Doubly Sparse: Sparse Mixture of Sparse Experts for Efficient Softmax Inference

Paper content:
Computations for the softmax function in neural network models are expensive when the number of output classes is large.
This can become a significant issue in both training and inference for such models.
In this paper, we present Doubly Sparse Softmax (DS-Softmax), Sparse Mixture of Sparse of Sparse Experts, to improve the efficiency for softmax inference.
During training, our method learns a two-level class hierarchy by dividing entire output class space into several partially overlapping experts.
Each expert is responsible for a learned subset of the output class space and each output class only belongs to a small number of those experts.
During inference, our method quickly locates the most probable expert to compute small-scale softmax.
Our method is learning-based and requires no knowledge of the output class partition space a priori.
We empirically evaluate our method on several real-world tasks and demonstrate that we can achieve significant computation reductions without loss of

assistant: text: The paper proposes the new Softmax algorithm implementation with two hierarchical levels of sparsity which speeds up the operation in language modeling.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Deep contextualized word representations

Paper content:
We introduce a new type of deep contextualized word representation that models both (1) complex characteristics of word use (e.g., syntax and semantics), and (2) how these uses vary across linguistic contexts (i.e., to model polysemy).
Our word vectors are learned functions of the internal states of a deep bidirectional language model (biLM), which is pretrained on a large text corpus.
We show that these representations can be easily added to existing models and significantly improve the state of the art across six challenging NLP problems, including question answering, textual entailment and sentiment analysis.
We also present an analysis showing that exposing the deep internals of the pretrained network is crucial, allowing downstream models to mix different types of semi-supervision signals.

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

Title: Reproducibility in Machine Learning for Health

Paper content:
Machine learning algorithms designed to characterize, monitor, and intervene on human health (ML4H) are expected to perform safely and reliably when operating at scale, potentially outside strict human supervision.
This requirement warrants a stricter attention to issues of reproducibility than other fields of machine learning.
In this work, we conduct a systematic evaluation of over 100 recently published ML4H research papers along several dimensions related to reproducibility we identified.
We find that the field of ML4H compares poorly to more established machine learning fields, particularly concerning data accessibility and code accessibility.
Finally, drawing from success in other fields of science, we propose recommendations to data providers, academic publishers, and the ML4H research community in order to promote reproducible research moving forward.

assistant: text: This paper conducts a quantitative and qualitative review of the state of the reproducibility for ML healthcare applications and proposes reccomendations to make research more reproducible.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Mixed Setting Training Methods for Incremental Slot-Filling Tasks

Paper content:
Model training remains a dominant financial cost and time investment in machine learning applications.
Developing and debugging models often involve iterative training, further exacerbating this issue.
With growing interest in increasingly complex models, there is a need for techniques that help to reduce overall training effort.
While incremental training can save substantial time and cost by training an existing model on a small subset of data, little work has explored policies for determining when incremental training provides adequate model performance versus full retraining.
We provide a method-agnostic algorithm for deciding when to incrementally train versus fully train.
We call this setting of non-deterministic full- or incremental training ``Mixed Setting Training".
Upon evaluation in slot-filling tasks, we find that this algorithm provides a bounded error, avoids catastrophic forgetting, and results in a significant speedup over a policy of always fully training.

assistant: text: This paper proposes an approach for deciding when to incrementally vs. fully retrain a model in the setting of iterative model development in slot filling tasks.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: V-MPO: On-Policy Maximum a Posteriori Policy Optimization for Discrete and Continuous Control

Paper content:
Some of the most successful applications of deep reinforcement learning to challenging domains in discrete and continuous control have used policy gradient methods in the on-policy setting.
However, policy gradients can suffer from large variance that may limit performance, and in practice require carefully tuned entropy regularization to prevent policy collapse.
As an alternative to policy gradient algorithms, we introduce V-MPO, an on-policy adaptation of Maximum a Posteriori Policy Optimization (MPO) that performs policy iteration based on a learned state-value function.
We show that V-MPO surpasses previously reported scores for both the Atari-57 and DMLab-30 benchmark suites in the multi-task setting, and does so reliably without importance weighting, entropy regularization, or population-based tuning of hyperparameters.
On individual DMLab and Atari levels, the proposed algorithm can achieve scores that are substantially higher than has previously been reported.
V-MPO is also applicable to problems with high-dimensional, continuous action spaces, which we demonstrate in the context of learning to control simulated humanoids with 22 degrees of freedom from full state observations and 56 degrees of freedom from pixel observations, as well as example OpenAI Gym tasks where V-MPO achieves substantially higher asymptotic scores than previously reported.

assistant: text: This paper presents an algorithm for on-policy reinforcement learning that can handle both continuous/discrete control, single/multi-task learning and use both low dimensional states and pixels.
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.
Recent advances in deep neural networks (DNN) has improved the performance of natural language processing tasks such as document classification, question answering, and sentiment analysis BID29 BID20 BID22 BID31 .
These approaches process the entire text and construct representations of words and phrases in order to perform target tasks.
While these models do realize high accuracy, their computational-time scales linearly with the size of the documents, which can be slow for documents containing many sentences.
In this context, various approaches based on modifying the existing RNN or LSTM architecture have been proposed BID21 ; BID31 to speed-up processing.
However, processing is still fundamentally sequential, which in turn requires loading entire documents to process, limiting compute gains.
We proposed a budgeted learning framework for learning a robust classifier under test-time budget constraints.
We demonstrated that training classifiers with data aggregation work well with low-complexity selectors based on word-embedding or bag-of-word model and achieve good performance with fragmented input.
The future work includes applying the proposed framework to other text reading tasks and improving the data aggregation strategy by applying learning to search approaches BID4 .

assistant: text: The authors consider training a RNN-based text classification where there is a resource restriction on test-time prediction, and provide an approach using a masking mechanism to reduce words/phrases/sentences used in prediction followed by a classifier to handle those components.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Approximation capability of neural networks on sets of probability measures and tree-structured data

Paper content:
This paper extends the proof of density of neural networks in the space of continuous (or even measurable) functions on Euclidean spaces to functions on compact sets of probability measures.
By doing so the work parallels a more then a decade old results on mean-map embedding of probability measures in reproducing kernel Hilbert spaces.
The work has wide practical consequences for multi-instance learning, where it theoretically justifies some recently proposed constructions.
The result is then extended to Cartesian products, yielding universal approximation theorem for tree-structured domains, which naturally occur in data-exchange formats like JSON, XML, YAML, AVRO, and ProtoBuffer.
This has important practical implications, as it enables to automatically create an architecture of neural networks for processing structured data (AutoML paradigms), as demonstrated by an accompanied library for JSON format.
This work has been motivated by recently proposed solutions to multi-instance learning BID28 ; BID19 ; BID5 and by mean-map embedding of probability measures BID23 .
It generalizes the universal approximation theorem of neural networks to compact sets of probability measures over compact subsets of Euclidean spaces.
Therefore, it can be seen as an adaptation of the mean-map framework to the world of neural networks, which is important for comparing probability measures and for multi-instance learning, and it proves the soundness of the constructions of BID19 ; BID5 .The
universal approximation theorem is extended to inputs with a tree schema (structure) which, being the basis of many data exchange formats like JSON, XML, ProtoBuffer, Avro, etc., are nowadays ubiquitous. This
theoretically justifies applications of (MIL) neural networks in this setting.As the presented proof relies on the Stone-Weierstrass theorem, it restricts non-linear functions in neural networks to be continuous in all but the last non-linear layer. Although
this does not have an impact on practical applications (all commonly use nonlinear functions within neural networks are continuous) it would be interesting to generalize the result to non-continuous non-linearities, as has been done for feed-forward neural networks in BID14 .

assistant: text: This paper investigates the approximation properties of a family of neural networks designed to address multi-instance learning problems, and shows that results for standard one layer architectures extend to these models.
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: A new Neural Network training procedure, designed for tabular data, that seeks to leverage feature clusters extracted from GBDTs.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: TabFact: A Large-scale Dataset for Table-based Fact Verification

Paper content:
The problem of verifying whether a textual hypothesis holds based on the given evidence, also known as fact verification, plays an important role in the study of natural language understanding and semantic representation.
However, existing studies are mainly restricted to dealing with unstructured evidence (e.g., natural language sentences and documents, news, etc), while verification under structured evidence, such as tables, graphs, and databases, remains unexplored.
This paper specifically aims to study the fact verification given semi-structured data as evidence.
To this end, we construct a large-scale dataset called TabFact with 16k Wikipedia tables as the evidence for 118k human-annotated natural language statements, which are labeled as either ENTAILED or REFUTED.
TabFact is challenging since it involves both soft linguistic reasoning and hard symbolic reasoning.
To address these reasoning challenges, we design two different models: Table-BERT and Latent Program Algorithm (LPA).
Table-BERT leverages the state-of-the-art pre-trained language model to encode the linearized tables and statements into continuous vectors for verification.
LPA parses statements into LISP-like programs and executes them against the tables to obtain the returned binary value for verification.
Both methods achieve similar accuracy but still lag far behind human performance.
We also perform a comprehensive analysis to demonstrate great future opportunities.

assistant: text: The authors propose the problem of fact verification with semi-structured data sources such as tables, create a new dataset, and evaluate baseline models with variations.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Domain Adaptation with Asymmetrically-Relaxed Distribution Alignment

Paper content:
Domain adaptation addresses the common problem when the target distribution generating our test data drifts from the source (training) distribution.
While absent assumptions, domain adaptation is impossible, strict conditions, e.g. covariate or label shift, enable principled algorithms.
Recently-proposed domain-adversarial approaches consist of aligning source and target encodings, often motivating this approach as minimizing two (of three) terms in a theoretical bound on target error.
Unfortunately, this minimization can cause arbitrary increases in the third term, e.g. they can break down under shifting label distributions.
We propose asymmetrically-relaxed distribution alignment, a new approach that overcomes some limitations of standard domain-adversarial algorithms.
Moreover, we characterize precise assumptions under which our algorithm is theoretically principled and demonstrate empirical benefits on both synthetic and real datasets.

assistant: text: This paper suggests relaxed metrics for domain adaptation which give new theoretical bounds on the target error.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: All SMILES Variational Autoencoder for Molecular Property Prediction and Optimization

Paper content:
Variational autoencoders (VAEs) defined over SMILES string and graph-based representations of molecules promise to improve the optimization of molecular properties, thereby revolutionizing the pharmaceuticals and materials industries.
However, these VAEs are hindered by the non-unique nature of SMILES strings and the computational cost of graph convolutions.
To efficiently pass messages along all paths through the molecular graph, we encode multiple SMILES strings of a single molecule using a set of stacked recurrent neural networks, harmonizing hidden representations of each atom between SMILES representations, and use attentional pooling to build a final fixed-length latent representation.
By then decoding to a disjoint set of SMILES strings of the molecule, our All SMILES VAE learns an almost bijective mapping between molecules and latent representations near the high-probability-mass subspace of the prior.
Our SMILES-derived but molecule-based latent representations significantly surpass the state-of-the-art in a variety of fully- and semi-supervised property regression and molecular property optimization tasks.

assistant: text: Method uses multiple inputs of SMILES strings, character-wise feature fusion across those strings, and network training through multiple output targets of SMILES strings, creating a robust fixed-length latent representation independent of SMILES variation.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Reproducing Meta-learning with differentiable closed-form solvers

Paper content:
In this paper, we present a reproduction of the paper of Bertinetto et al. [2019] "Meta-learning with differentiable closed-form solvers" as part of the ICLR 2019 Reproducibility Challenge.
In successfully reproducing the most crucial part of the paper, we reach a performance that is comparable with or superior to the original paper on two benchmarks for several settings.
We evaluate new baseline results, using a new dataset presented in the paper.
Yet, we also provide multiple remarks and recommendations about reproducibility and comparability.
After we brought our reproducibility work to the authors’ attention, they have updated the original paper on which this work is based and released code as well.
Our contributions mainly consist in reproducing the most important results of their original paper, in giving insight in the reproducibility and in providing a first open-source implementation.

assistant: text: We successfully reproduce and give remarks on the comparison with baselines of a meta-learning approach for few-shot classification that works by backpropagating through the solution of a closed-form solver.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Diffusing Policies : Towards Wasserstein Policy Gradient Flows

Paper content:
Policy gradients methods often achieve better performance when the change in policy is limited to a small Kullback-Leibler divergence.
We derive policy gradients where the change in policy is limited to a small Wasserstein distance (or trust region).
This is done in the discrete and continuous multi-armed bandit settings with entropy regularisation.
We show that in the small steps limit with respect to the Wasserstein distance $W_2$, policy dynamics are governed by the heat equation, following the Jordan-Kinderlehrer-Otto result.
This means that policies undergo diffusion and advection, concentrating near actions with high reward.
This helps elucidate the nature of convergence in the probability matching setup, and provides justification for empirical practices such as Gaussian policy priors and additive gradient noise.

assistant: text: The paper explores the connections between reinforcement learning and the theory of quadratic optimal transport