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

Title: CAN ALTQ LEARN FASTER: EXPERIMENTS AND THEORY

Paper content:
Differently from the popular Deep Q-Network (DQN) learning, Alternating Q-learning (AltQ) does not fully fit a target Q-function at each iteration, and is generally known to be unstable and inefficient.
Limited applications of AltQ mostly rely on substantially altering the algorithm architecture in order to improve its performance.
Although Adam appears to be a natural solution, its performance in AltQ has rarely been studied before.
In this paper, we first provide a solid exploration on how well AltQ performs with Adam.
We then take a further step to improve the implementation by adopting the technique of parameter restart.
More specifically, the proposed algorithms are tested on a batch of Atari 2600 games and exhibit superior performance than the DQN learning method.
The convergence rate of the slightly modified version of the proposed algorithms is characterized under the linear function approximation.
To the best of our knowledge, this is the first theoretical study on the Adam-type algorithms in Q-learning.

assistant: text: New Experiments and Theory for Adam Based Q-Learning
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Rethinking generalization requires revisiting old ideas: statistical mechanics approaches and complex learning behavior

Paper content:
We describe an approach to understand the peculiar and counterintuitive generalization properties of deep neural networks.
The approach involves going beyond worst-case theoretical capacity control frameworks that have been popular in machine learning in recent years to revisit old ideas in the statistical mechanics of neural networks.
Within this approach, we present a prototypical Very Simple Deep Learning (VSDL) model, whose behavior is controlled by two control parameters, one describing an effective amount of data, or load, on the network (that decreases when noise is added to the input), and one with an effective temperature interpretation (that increases when algorithms are early stopped).
Using this model, we describe how a very simple application of ideas from the statistical mechanics theory of generalization provides a strong qualitative description of recently-observed empirical results regarding the inability of deep neural networks not to overfit training data, discontinuous learning and sharp transitions in the generalization properties of learning algorithms, etc.

assistant: text: Rethinking generalization requires revisiting old ideas: statistical mechanics approaches and complex learning behavior
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Joint autoencoders: a flexible meta-learning framework

Paper content:
The incorporation of prior knowledge into learning is essential in achieving good performance based on small noisy samples.
Such knowledge is often incorporated through the availability of related data arising from domains and tasks similar to the one of current interest.
Ideally one would like to allow both the data for the current task and for previous related tasks to self-organize the learning system in such a way that commonalities and differences between the tasks are learned in a data-driven fashion.
We develop a framework for learning multiple tasks simultaneously, based on sharing features that are common to all tasks, achieved through the use of a modular deep feedforward neural network consisting of shared branches, dealing with the common features of all tasks, and private branches, learning the specific unique aspects of each task.
Once an appropriate weight sharing architecture has been established, learning takes place through standard algorithms for feedforward networks, e.g., stochastic gradient descent and its variations.
The method deals with meta-learning (such as domain adaptation, transfer and multi-task learning) in a unified fashion, and can easily deal with data arising from different types of sources.
Numerical experiments demonstrate the effectiveness of learning in domain adaptation and transfer learning setups, and provide evidence for the flexible and task-oriented representations arising in the network.

assistant: text: A generic framework for handling transfer and multi-task learning using pairs of autoencoders with task-specific and shared weights.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Discovering Low-Precision Networks Close to Full-Precision Networks for Efficient Embedded Inference

Paper content:
To realize the promise of ubiquitous embedded deep network inference, it is essential to seek limits of energy and area efficiency.
To this end, low-precision networks offer tremendous promise because both energy and area scale down quadratically with the reduction in precision.
Here, for the first time, we demonstrate ResNet-18, ResNet-34, ResNet-50, ResNet-152, Inception-v3, densenet-161, and VGG-16bn networks on the ImageNet classification benchmark that, at 8-bit precision exceed the accuracy of the full-precision baseline networks after one epoch of finetuning, thereby leveraging the availability of pretrained models.
We also demonstrate ResNet-18, ResNet-34, and ResNet-50 4-bit models that match the accuracy of the full-precision baseline networks -- the highest scores to date.
Surprisingly, the weights of the low-precision networks are very close (in cosine similarity) to the weights of the corresponding baseline networks, making training from scratch unnecessary.
We find that gradient noise due to quantization during training increases with reduced precision, and seek ways to overcome this noise.
The number of iterations required by stochastic gradient descent to achieve a given training error is related to the square of (a) the distance of the initial solution from the final plus (b) the maximum variance of the gradient estimates.
By drawing inspiration from this observation, we (a) reduce solution distance by starting with pretrained fp32 precision baseline networks and fine-tuning, and (b) combat noise introduced by quantizing weights and activations during training, by using larger batches along with matched learning rate annealing.
Sensitivity analysis indicates that these techniques, coupled with proper activation function range calibration, offer a promising heuristic to discover low-precision networks, if they exist, close to fp32 precision baseline networks.

assistant: text: This paper proposes to improve the performance of low-precision models by doing quantization on pre-trained models, using large batches size, and using proper learning rate annealing with longer training time.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Hierarchical Summary-to-Article Generation

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

assistant: text: To address the issue of degeneration in summary-to-article generation, this paper proposes a hierarchical generation approach which first generates an intermediate sketch of the article and then the full article.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: One-shot learning: From domain knowledge to action models

Paper content:
Most approaches to learning action planning models heavily rely on a significantly large volume of training samples or plan observations.
In this paper, we adopt a different approach based on deductive learning from domain-specific knowledge, specifically from logic formulae that specify constraints about the possible states of a given domain.
The minimal input observability required by our approach is a single example composed of a full initial state and a partial goal state.
We will show that exploiting specific domain knowledge enables to constrain the space of possible action models as well as to complete partial observations, both of which turn out helpful to learn good-quality action models.

assistant: text: A domain acquisition approach that considers using a different representation for the partial domain model by using schematic mutex relations in place of pre/post conditions.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Unsupervised  one-to-many image translation

Paper content:
We perform completely unsupervised one-sided image to image translation between a source domain $X$ and a target domain $Y$ such that we preserve relevant underlying shared semantics (e.g., class, size, shape, etc).
In particular, we are interested in a more difficult case than those typically addressed in the literature, where the source and target are ``far" enough that reconstruction-style or pixel-wise approaches fail.
We argue that transferring (i.e., \emph{translating}) said relevant information should involve both discarding source domain-specific information while incorporate target domain-specific information, the latter of which we model with a noisy prior distribution.
In order to avoid the degenerate case where the generated samples are only explained by the prior distribution, we propose to minimize an estimate of the mutual information between the generated sample and the sample from the prior distribution.
We discover that the architectural choices are an important factor to consider in order to preserve the shared semantic between $X$ and $Y$.
We show state of the art results on the MNIST to SVHN task for unsupervised image to image translation.

assistant: text: We train an image to image translation network that take as input the source image and a sample from a prior distribution to generate a sample from the target distribution
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: RotationOut as a Regularization Method for Neural Network

Paper content:
In this paper, we propose a novel regularization method, RotationOut, for neural networks.
Different from Dropout that handles each neuron/channel independently, RotationOut regards its input layer as an entire vector and introduces regularization by randomly rotating the vector.
RotationOut can also be used in convolutional layers and recurrent layers with a small modification.
We further use a noise analysis method to interpret the difference between RotationOut and Dropout in co-adaptation reduction.
Using this method, we also show how to use RotationOut/Dropout together with Batch Normalization.
Extensive experiments in vision and language tasks are conducted to show the effectiveness of the proposed method.
Codes will be available.

assistant: text: We propose a regularization method for neural network and a noise analysis method
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Semantic Code Repair using Neuro-Symbolic Transformation Networks

Paper content:
We study the problem of semantic code repair, which can be broadly defined as automatically fixing non-syntactic bugs in source code.
The majority of past work in semantic code repair assumed access to unit tests against which candidate repairs could be validated.
In contrast, the goal here is to develop a strong statistical model to accurately predict both bug locations and exact fixes without access to information about the intended correct behavior of the program.
Achieving such a goal requires a robust contextual repair model, which we train on a large corpus of real-world source code that has been augmented with synthetically injected bugs.
Our framework adopts a two-stage approach where first a large set of repair candidates are generated by rule-based processors, and then these candidates are scored by a statistical model using a novel neural network architecture which we refer to as Share, Specialize, and Compete.
Specifically, the architecture (1) generates a  shared encoding of the source code using an RNN over the abstract syntax tree, (2) scores each candidate repair using specialized network modules, and (3) then normalizes these scores together so they can compete against one another in comparable probability space.
We evaluate our model on a real-world test set gathered from GitHub containing four common categories of bugs.
Our model is able to predict the exact correct repair 41% of the time with a single guess, compared to 13% accuracy for an attentional sequence-to-sequence model.

assistant: text: A neural architecture for scoring and ranking program repair candidates to perform semantic program repair statically without access to unit tests.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Overcoming Multi-model Forgetting

Paper content:
We identify a phenomenon, which we refer to as *multi-model forgetting*, that occurs when sequentially training multiple deep networks with partially-shared parameters; the performance of previously-trained models degrades as one optimizes a subsequent one, due to the overwriting of shared parameters.
To overcome this, we introduce a statistically-justified weight plasticity loss that regularizes the learning of a model's shared parameters according to their importance for the previous models, and demonstrate its effectiveness when training two models sequentially and for neural architecture search.
Adding weight plasticity in neural architecture search preserves the best models to the end of the search and yields improved results in both natural language processing and computer vision tasks.

assistant: text: This paper discusses the phenomena of “neural brainwashing”, which refers to that the performance of one model is affected via another model sharing model parameters.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Skip Connections Eliminate Singularities

Paper content:
Skip connections made the training of very deep networks possible and have become an indispensable component in a variety of neural architectures.
A completely satisfactory explanation for their success remains elusive.
Here, we present a novel explanation for the benefits of skip connections in training very deep networks.
The difficulty of training deep networks is partly due to the singularities caused by the non-identifiability of the model.
Several such singularities have been identified in previous works:
(i) overlap singularities caused by the permutation symmetry of nodes in a given layer,
(ii) elimination singularities corresponding to the elimination, i.e. consistent deactivation, of nodes,
(iii) singularities generated by the linear dependence of the nodes.
These singularities cause degenerate manifolds in the loss landscape that slow down learning.
We argue that skip connections eliminate these singularities by breaking the permutation symmetry of nodes, by reducing the possibility of node elimination and by making the nodes less linearly dependent.
Moreover, for typical initializations, skip connections move the network away from the "ghosts" of these singularities and sculpt the landscape around them to alleviate the learning slow-down.
These hypotheses are supported by evidence from simplified models, as well as from experiments with deep networks trained on real-world datasets.

assistant: text: Degenerate manifolds arising from the non-identifiability of the model slow down learning in deep networks; skip connections help by breaking degeneracies.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Amharic Light Stemmer

Paper content:
Stemming is the process of removing affixes( i.e. prefixes, infixes and suffixes) that improve the accuracy and performance of information retrieval systems.This paper presents the reduction of Amharic words to corresponding stem where with the intention that it preserves semantic information.
The proposed approach efficiently removes affixes from an Amharic word.
The process of removing such affixes (prefixes, infixes and suffixes) from a word to its base form is called stemming.
While many stemmers exist for dominant languages such as English, under resourced languages such as Amharic which lacks such powerful tool support.
In this paper, we design a light Amharic stemmer relying on the rules that receives an Amharic word and then it finds a match to the beginning of a word to the possible prefixes and to its ending with the possible suffixes and finally it checks whether it has infix.
The final result is the stem if there is any prefix, infix or/and suffix, otherwise it remains in one of the earlier states.
The technique does not rely on any additional resource (e.g. dictionary) to verify the generated stem.
The performance of the generated stemmer is evaluated using manually annotated Amharic words.
The result is compared with current state-of-the-art stemmer for Amharic showing an increase of 7% in stemmer correctness.

assistant: text: This paper studies the stemming for morphologically rich languages with a light stemmer that only removes affixes to the extent that the original semantic information in the word is kept.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: SuperCaptioning: Image Captioning Using Two-dimensional Word Embedding

Paper content:
Language and vision are processed as two different modal in current work for image captioning.
However, recent work on Super Characters method shows the effectiveness of two-dimensional word embedding, which converts text classification problem into image classification problem.
In this paper, we propose the SuperCaptioning method, which borrows the idea of two-dimensional word embedding from Super Characters method, and processes the information of language and vision together in one single CNN model.
The experimental results on Flickr30k data shows the proposed method gives high quality image captions.
An interactive demo is ready to show at the workshop.

assistant: text: Image captioning using two-dimensional word embedding.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Sparse Coding with Gated Learned ISTA

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

assistant: text: This paper is focused on solving sparse coding problems using LISTA-type networks by proposing a "gain gating function" to mitigate the weakness of the "no false positive" assumption.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Adaptive Neural Trees

Paper content:
Deep neural networks and decision trees operate on largely separate paradigms; typically, the former performs representation learning with pre-specified architectures, while the latter is characterised by learning hierarchies over pre-specified features with data-driven architectures.
We unite the two via adaptive neural trees (ANTs), a model that incorporates representation learning into edges, routing functions and leaf nodes of a decision tree, along with a backpropagation-based training algorithm that adaptively grows the architecture from primitive modules (e.g., convolutional layers).
ANTs allow increased interpretability via hierarchical clustering, e.g., learning meaningful class associations, such as separating natural vs. man-made objects.
We demonstrate this on classification and regression tasks, achieving over 99% and 90% accuracy on the MNIST and CIFAR-10 datasets, and outperforming standard neural networks, random forests and gradient boosted trees on the SARCOS dataset.
Furthermore, ANT optimisation naturally adapts the architecture to the size and complexity of the training data.

assistant: text: This paper proposes the Adaptive Neural Trees approach to combine the two learning paradigms of deep neural nets and decision trees
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: CyCADA: Cycle-Consistent Adversarial Domain Adaptation

Paper content:
Domain adaptation is critical for success in new, unseen environments.
Adversarial adaptation models applied in feature spaces discover domain invariant representations, but are difficult to visualize and sometimes fail to capture pixel-level and low-level domain shifts.
Recent work has shown that generative adversarial networks combined with cycle-consistency constraints are surprisingly effective at  mapping images between domains, even without the use of aligned image pairs.
We propose a novel discriminatively-trained Cycle-Consistent Adversarial Domain Adaptation model.
CyCADA adapts representations at both the pixel-level and feature-level, enforces cycle-consistency while leveraging a task loss, and does not require aligned pairs.
Our model can be applied in a variety of visual recognition and prediction settings.
We show new state-of-the-art results across multiple adaptation tasks, including digit classification and semantic segmentation of road scenes demonstrating transfer from synthetic to real world domains.

assistant: text: An unsupervised domain adaptation approach which adapts at both the pixel and feature levels
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Learning to Learn via Gradient Component Corrections

Paper content:
Gradient-based meta-learning algorithms require several steps of gradient descent to adapt to newly incoming tasks.
This process becomes more costly as the number  of  samples  increases.
Moreover,  the  gradient  updates  suffer  from  several sources of noise leading to a degraded performance.
In this work,  we propose a meta-learning algorithm equipped with the GradiEnt Component COrrections, aGECCO cell for short, which generates a multiplicative corrective low-rank matrix which (after vectorization) corrects the estimated gradients.
GECCO contains a simple decoder-like network with learnable parameters, an attention module and a so-called context input parameter.
The context parameter of GECCO is updated to  generate  a  low-rank  corrective  term  for  the  network  gradients.
As  a  result, meta-learning requires only a few of gradient updates to absorb new task (often, a single update is sufficient in the few-shot scenario).
While previous approaches address this problem by altering the learning rates, factorising network parameters or directly learning feature corrections from features and/or gradients, GECCO is an off-the-shelf generator-like unit that performs element-wise gradient corrections without the need to ‘observe’ the features and/or the gradients directly.
We show that our GECCO (i) accelerates learning, (ii) performs robust corrections of the gradients corrupted by a noise, and (iii) leads to notable improvements over existing gradient-based meta-learning algorithms.

assistant: text: This paper proposes a method to meta-learn a gradient correction module in which preconditioning is parameterized by a neural network, and builds in a two-stage gradient update process during adaptation.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Algorithmic Framework for Model-based Deep Reinforcement Learning with Theoretical Guarantees

Paper content:
Model-based reinforcement learning (RL) is considered to be a promising approach to reduce the sample complexity that hinders model-free RL.
However, the theoretical understanding of such methods has been rather limited.
This paper introduces a novel algorithmic framework for designing and analyzing model-based RL algorithms with theoretical guarantees.
We design a meta-algorithm with a theoretical guarantee of monotone improvement to a local maximum of the expected reward.
The meta-algorithm iteratively builds a lower bound of the expected reward based on the estimated dynamical model and sample trajectories, and then maximizes the lower bound jointly over the policy and the model.
The framework extends the optimism-in-face-of-uncertainty principle to non-linear dynamical models in a way that requires no explicit uncertainty quantification.
Instantiating our framework with simplification gives a  variant of model-based RL algorithms Stochastic Lower Bounds Optimization (SLBO).
Experiments demonstrate that SLBO achieves the state-of-the-art performance when only 1M or fewer samples are permitted on a range of continuous control benchmark tasks.

assistant: text: The paper proposed a framework to design model-based RL algorithms based on OFU that achieves SOTA performance on MuJoCo tasks.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: A Fine-Grained Spectral Perspective on Neural Networks

Paper content:
Are neural networks biased toward simple functions?
Does depth always help learn more complex features?
Is training the last layer of a network as good as training all layers?
These questions seem unrelated at face value, but in this work we give all of them a common treatment from the spectral perspective.
We will study the spectra of the *Conjugate Kernel, CK,* (also called the *Neural Network-Gaussian Process Kernel*), and the *Neural Tangent Kernel, NTK*.
Roughly, the CK and the NTK tell us respectively ``"what a network looks like at initialization" and "``what a network looks like during and after training."
Their spectra then encode valuable information about the initial distribution and the training and generalization properties of neural networks.
By analyzing the eigenvalues, we lend novel insights into the questions put forth at the beginning, and we verify these insights by extensive experiments of neural networks.
We believe the computational tools we develop here for analyzing the spectra of CK and NTK serve as a solid foundation for future studies of deep neural networks.
We have open-sourced the code for it and for generating the plots in this paper at github.com/jxVmnLgedVwv6mNcGCBy/NNspectra.

assistant: text: This paper gives a spectral analysis on neural networks' conjugate kernel and neural tangent kernel on boolean cube to resolve why deep networks are biased towards simple functions.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Probabilistic View of Multi-agent Reinforcement Learning: A Unified Approach

Paper content:
Formulating the reinforcement learning (RL) problem in the framework of probabilistic inference not only offers a new perspective about RL, but also yields practical algorithms that are more robust and easier to train.
While this connection between RL and probabilistic inference has been extensively studied in the single-agent setting, it has not yet been fully understood in the multi-agent setup.
In this paper, we pose the problem of multi-agent reinforcement learning as the problem of performing inference in a particular graphical model.
We model the environment, as seen by each of the agents, using separate but related Markov decision processes.
We derive a practical off-policy maximum-entropy actor-critic algorithm that we call Multi-agent Soft Actor-Critic (MA-SAC) for performing approximate inference in the proposed model using variational inference.
MA-SAC can be employed in both cooperative and competitive settings.
Through experiments, we demonstrate that MA-SAC outperforms a strong baseline on several multi-agent scenarios.
While MA-SAC is one resultant multi-agent RL algorithm that can be derived from the proposed probabilistic framework, our work provides a unified view of maximum-entropy algorithms in the multi-agent setting.

assistant: text: This paper proposes a new algorithm named Multi-Agent Soft Actor-Critic (MA-SAC) based on the off-policy maximum-entropy actor critic algorithm Soft Actor-Critic (SAC)
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Diversity and Depth in Per-Example Routing Models

Paper content:
Routing models, a form of conditional computation where examples are routed through a subset of components in a larger network, have shown promising results in recent works.
Surprisingly, routing models to date have lacked important properties, such as architectural diversity and large numbers of routing decisions.
Both architectural diversity and routing depth can increase the representational power of a routing network.
In this work, we address both of these deficiencies.
We discuss the significance of architectural diversity in routing models, and explain the tradeoffs between capacity and optimization when increasing routing depth.
In our experiments, we find that adding architectural diversity to routing models significantly improves performance, cutting the error rates of a strong baseline by 35% on an Omniglot setup.
However, when scaling up routing depth, we find that modern routing techniques struggle with optimization.
We conclude by discussing both the positive and negative results, and suggest directions for future research.

assistant: text: Adds diversity to the type of architectural unit available for the router at each decision and scaling to deeper networks, achieving state of the art performance on Omniglot.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Variational Gaussian Process Models without Matrix Inverses

Paper content:
Large matrix inversions have often been cited as a major impediment to scaling Gaussian process (GP) models.
With the use of GPs as building blocks for ever more sophisticated Bayesian deep learning models, removing these impediments is a necessary step for achieving large scale results.
We present a variational approximation for a wide range of GP models that does not require a matrix inverse to be performed at each optimisation step.
Our bound instead directly parameterises a free matrix, which is an additional variational parameter.
At the local maxima of the bound, this matrix is equal to the matrix inverse.
We prove that our bound gives the same guarantees as earlier variational approximations.
We demonstrate some beneficial properties of the bound experimentally, although significant wall clock time speed improvements will require future improvements in optimisation and implementation.

assistant: text: We present a variational lower bound for GP models that can be optimised without computing expensive matrix operations like inverses, while providing the same guarantees as existing variational approximations.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: B-Spline CNNs on Lie groups

Paper content:
Group convolutional neural networks (G-CNNs) can be used to improve classical CNNs by equipping them with the geometric structure of groups.
Central in the success of G-CNNs is the lifting of feature maps to higher dimensional disentangled representations, in which data characteristics are effectively learned, geometric data-augmentations are made obsolete, and predictable behavior under geometric transformations (equivariance) is guaranteed via group theory.
Currently, however, the practical implementations of G-CNNs are limited to either discrete groups (that leave the grid intact) or continuous compact groups such as rotations (that enable the use of Fourier theory).
In this paper we lift these limitations and propose a modular framework for the design and implementation of G-CNNs for arbitrary Lie groups.
In our approach the differential structure of Lie groups is used to expand convolution kernels in a generic basis of B-splines that is defined on the Lie algebra.
This leads to a flexible framework that enables localized, atrous, and deformable convolutions in G-CNNs by means of respectively localized, sparse and non-uniform B-spline expansions.
The impact and potential of our approach is studied on two benchmark datasets: cancer detection in histopathology slides (PCam dataset) in which rotation equivariance plays a key role and facial landmark localization (CelebA dataset) in which scale equivariance is important.
In both cases, G-CNN architectures outperform their classical 2D counterparts and the added value of atrous and localized group convolutions is studied in detail.

assistant: text: A framework for building group CNN with an arbitrary Lie group G, which shows superiority over a CNN in tumor classification and landmark localization.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Top-Down Neural Model For Formulae

Paper content:
We present a simple neural model that given a formula and a property tries to answer the question whether the formula has the given property, for example whether a propositional formula is always true.
The structure of the formula is captured by a feedforward neural network recursively built for the given formula in a top-down manner.
The results of this network are then processed by two recurrent neural networks.
One of the interesting aspects of our model is how propositional atoms are treated.
For example, the model is insensitive to their names, it only matters whether they are the same or distinct.

assistant: text: This paper provides a new neural-net model of logical formulae that gathers information about a given formula by traversing its parse tree top-down.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Overcoming Multi-model Forgetting

Paper content:
We identify a phenomenon, which we refer to as *multi-model forgetting*, that occurs when sequentially training multiple deep networks with partially-shared parameters; the performance of previously-trained models degrades as one optimizes a subsequent one, due to the overwriting of shared parameters.
To overcome this, we introduce a statistically-justified weight plasticity loss that regularizes the learning of a model's shared parameters according to their importance for the previous models, and demonstrate its effectiveness when training two models sequentially and for neural architecture search.
Adding weight plasticity in neural architecture search preserves the best models to the end of the search and yields improved results in both natural language processing and computer vision tasks.

assistant: text: We identify a phenomenon, neural brainwashing, and introduce a statistically-justified weight plasticity loss to overcome this.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Learning Document Embeddings With CNNs

Paper content:
This paper proposes a new model for document embedding.
Existing approaches either require complex inference or use recurrent neural networks that are difficult to parallelize.
We take a different route and use recent advances in language modeling to develop a convolutional neural network embedding model.
This allows us to train deeper architectures that are fully parallelizable.
Stacking layers together increases the receptive filed allowing each successive layer to model increasingly longer range semantic dependences within the document.
Empirically we demonstrate superior results on two publicly available benchmarks.
Full code will be released with the final version of this paper.

assistant: text: Introduces a new model for the general task of inducing document representations (embeddings) which uses a CNN architecture to improve computational efficiency.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Accumulation Bit-Width Scaling For Ultra-Low Precision Training Of Deep Networks

Paper content:
Efforts to reduce the numerical precision of computations in deep learning training have yielded systems that aggressively quantize weights and activations, yet employ wide high-precision accumulators for partial sums in inner-product operations to preserve the quality of convergence.
The absence of any framework to analyze the precision requirements of partial sum accumulations results in conservative design choices.
This imposes an upper-bound on the reduction of complexity of multiply-accumulate units.
We present a statistical approach to analyze the impact of reduced accumulation precision on deep learning training.
Observing that a bad choice for accumulation precision results in loss of information that manifests itself as a reduction in variance in an ensemble of partial sums, we derive a set of equations that relate this variance to the length of accumulation and the minimum number of bits needed for accumulation.
We apply our analysis to three benchmark networks: CIFAR-10 ResNet 32, ImageNet ResNet 18 and ImageNet AlexNet.
In each case, with accumulation precision set in accordance with our proposed equations, the networks successfully converge to the single precision floating-point baseline.
We also show that reducing accumulation precision further degrades the quality of the trained network, proving that our equations produce tight bounds.
Overall this analysis enables precise tailoring of computation hardware to the application, yielding area- and power-optimal systems.

assistant: text: The authors propose an analytical method to predict the number of mantissa bits needed for partial summations for convolutional and fully connected layers
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Difference-Seeking Generative Adversarial Network

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

assistant: text: This paper presents DS-GAN, which aims to learn the difference between any two distributions whose samples are difficult or impossible to collect, and shows its effectiveness on semi-supervised learning and adversarial training 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: The paper proposes an online variant of MPO, V-MPO, which learns the V-function and updates the non-parametric distribution towards the advantages.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Self-Monitoring Navigation Agent via Auxiliary Progress Estimation

Paper content:
The Vision-and-Language Navigation (VLN) task entails an agent following navigational instruction in photo-realistic unknown environments.
This challenging task demands that the agent be aware of which instruction was completed, which instruction is needed next, which way to go, and its navigation progress towards the goal.
In this paper, we introduce a self-monitoring agent with two complementary components: (1) visual-textual co-grounding module to locate the instruction completed in the past, the instruction required for the next action, and the next moving direction from surrounding images and (2) progress monitor to ensure the grounded instruction correctly reflects the navigation progress.
We test our self-monitoring agent on a standard benchmark and analyze our proposed approach through a series of ablation studies that elucidate the contributions of the primary components.
Using our proposed method, we set the new state of the art by a significant margin (8% absolute increase in success rate on the unseen test set).
Code is available at https://github.com/chihyaoma/selfmonitoring-agent.

assistant: text: A method for vision+language navigation which tracks progress on the instruction using a progress monitor and a visual-textual co-grounding module, and performs well on standard benchmarks.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Learning Time-Aware Assistance Functions for Numerical Fluid Solvers

Paper content:
Improving the accuracy of numerical methods remains a central challenge in many disciplines and is especially important for nonlinear simulation problems.
A representative example of such problems is fluid flow, which has been thoroughly studied to arrive at efficient simulations of complex flow phenomena.
This paper presents a data-driven approach that learns to improve the accuracy of numerical solvers.
The proposed method utilizes an advanced numerical scheme with a fine simulation resolution to acquire reference data.
We, then, employ a neural network that infers a correction to move a coarse thus quickly obtainable result closer to the reference data.
We provide insights into the targeted learning problem with different learning approaches: fully supervised learning methods with a naive and an optimized data acquisition as well as an unsupervised learning method with a differentiable Navier-Stokes solver.
While our approach is very general and applicable to arbitrary partial differential equation models, we specifically highlight gains in accuracy for fluid flow simulations.

assistant: text: The authors aim at improving the accuracy of numerical solvers by training a neural network on simulated reference data which corrects the numerical solver.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Learning to Control Visual Abstractions for Structured Exploration in Deep Reinforcement Learning

Paper content:
Exploration in environments with sparse rewards is a key challenge for reinforcement learning.
How do we design agents with generic inductive biases so that they can explore in a consistent manner instead of just using local exploration schemes like epsilon-greedy?
We propose an unsupervised reinforcement learning agent which learns a discrete pixel grouping model that preserves spatial geometry of the sensors and implicitly of the environment as well.
We use this representation to derive geometric intrinsic reward functions, like centroid coordinates and area, and learn policies to control each one of them with off-policy learning.
These policies form a basis set of behaviors (options) which allows us explore in a consistent way and use them in a hierarchical reinforcement learning setup to solve for extrinsically defined rewards.
We show that our approach can scale to a variety of domains with competitive performance, including navigation in 3D environments and Atari games with sparse rewards.

assistant: text: The paper introduces visual abstractions that are used for reinforcement learning, where an algorithm learns to "control" each abstraction as well as select the options to achieve the overall task.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Combining adaptive algorithms and hypergradient method: a performance and robustness study

Paper content:
Wilson et al. (2017) showed that, when the stepsize schedule is properly designed, stochastic gradient generalizes better than ADAM (Kingma & Ba, 2014).
In light of recent work on hypergradient methods (Baydin et al., 2018), we revisit these claims to see if such methods close the gap between the most popular optimizers.
As a byproduct, we analyze the true benefit of these hypergradient methods compared to more classical schedules, such as the fixed decay of Wilson et al. (2017).
In particular, we observe they are of marginal help since their performance varies significantly when tuning their hyperparameters.
Finally, as robustness is a critical quality of an optimizer, we provide a sensitivity analysis of these gradient based optimizers to assess how challenging their tuning is.

assistant: text: We provide a study trying to see how the recent online learning rate adaptation extends the conclusion made by Wilson et al. 2018 about adaptive gradient methods, along with comparison and sensitivity analysis.