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

Title: Relational Neural Expectation Maximization: Unsupervised Discovery of Objects and their Interactions

Paper content:
Common-sense physical reasoning is an essential ingredient for any intelligent agent operating in the real-world.
For example, it can be used to simulate the environment, or to infer the state of parts of the world that are currently unobserved.
In order to match real-world conditions this causal knowledge must be learned without access to supervised data.
To address this problem we present a novel method that learns to discover objects and model their physical interactions from raw visual images in a purely unsupervised fashion.
It incorporates prior knowledge about the compositional nature of human perception to factor interactions between object-pairs and learn efficiently.
On videos of bouncing balls we show the superior modelling capabilities of our method compared to other unsupervised neural approaches that do not incorporate such prior knowledge.
We demonstrate its ability to handle occlusion and show that it can extrapolate learned knowledge to scenes with different numbers of objects.

assistant: text: We introduce a novel approach to common-sense physical reasoning that learns to discover objects and model their physical interactions from raw visual images in a purely unsupervised fashion
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Improving Generalization and Stability of Generative Adversarial Networks

Paper content:
Generative Adversarial Networks (GANs) are one of the most popular tools for learning complex high dimensional distributions.
However, generalization properties of GANs have not been well understood.
In this paper, we analyze the generalization of GANs in practical settings.
We show that discriminators trained on discrete datasets with the original GAN loss have poor generalization capability and do not approximate the theoretically optimal discriminator.
We propose a zero-centered gradient penalty for improving the generalization of the discriminator by pushing it toward the optimal discriminator.
The penalty guarantees the generalization and convergence of GANs.
Experiments on synthetic and large scale datasets verify our theoretical analysis.

assistant: text: We propose a zero-centered gradient penalty for improving generalization and stability of GANs
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: On Learning Wire-Length Efficient Neural Networks

Paper content:
Pruning neural networks for wiring length efficiency is considered.
Three techniques are proposed and experimentally tested: distance-based regularization, nested-rank pruning, and layer-by-layer bipartite matching.
The first two algorithms are used in the training and pruning phases, respectively, and the third is used in the arranging neurons phase.
Experiments show that distance-based regularization with weight based pruning tends to perform the best, with or without layer-by-layer bipartite matching.
These results suggest that these techniques may be useful in creating neural networks for implementation in widely deployed specialized circuits.

assistant: text: Three new algorithms with ablation studies to prune neural network to optimize for wiring length, as opposed to number of remaining weights.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Domain-Relevant Embeddings for Question Similarity

Paper content:
The rate at which medical questions are asked online significantly exceeds the capacity of qualified people to answer them, leaving many questions unanswered or inadequately answered.
Many of these questions are not unique, and reliable identification of similar questions would enable more efficient and effective question answering schema.
While many research efforts have focused on the problem of general question similarity, these approaches do not generalize well to the medical domain, where medical expertise is often required to determine semantic similarity.
In this paper, we show how a semi-supervised approach of pre-training a neural network on medical question-answer pairs is a particularly useful intermediate task for the ultimate goal of determining medical question similarity.
While other pre-training tasks yield an accuracy below 78.7% on this task, our model achieves an accuracy of 82.6% with the same number of training examples, an accuracy of 80.0% with a much smaller training set, and an accuracy of 84.5% when the full corpus of medical question-answer data is used.

assistant: text: We show that question-answer matching is a particularly good pre-training task for question-similarity and release a dataset for medical question similarity
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: PVAE: Learning Disentangled Representations with Intrinsic Dimension via Approximated L0 Regularization

Paper content:
Many models based on the Variational Autoencoder are proposed to achieve disentangled latent variables in inference.
However, most current work is focusing on designing powerful disentangling regularizers, while the given number of dimensions for the latent representation at initialization could severely inﬂuence the disentanglement.
Thus, a pruning mechanism is introduced, aiming at automatically seeking for the intrinsic dimension of the data while promoting disentangled representations.
The proposed method is validated on MPI3D and MNIST to be advancing state-of-the-art methods in disentanglement, reconstruction, and robustness.
The code is provided on the https://github.com/WeyShi/FYP-of-Disentanglement.

assistant: text: The Pruning VAE is proposed to search for disentangled variables with intrinsic dimension.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Learnability of Learned Neural Networks

Paper content:
This paper explores the simplicity of learned neural networks under various settings: learned on real vs random data, varying size/architecture and using large minibatch size vs small minibatch size.
The notion of simplicity used here is that of learnability i.e., how accurately can the prediction function of a neural network be learned from labeled samples from it.
While learnability is different from (in fact often higher than) test accuracy, the results herein suggest that there is a strong correlation between small generalization errors and high learnability.
This work also shows that there exist significant qualitative differences in shallow networks as compared to popular deep networks.
More broadly, this paper extends in a new direction, previous work on understanding the properties of learned neural networks.
Our hope is that such an empirical study of understanding learned neural networks might shed light on the right assumptions that can be made for a theoretical study of deep learning.

assistant: text: Exploring the Learnability of Learned Neural Networks
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: ProxQuant: Quantized Neural Networks via Proximal Operators

Paper content:
To make deep neural networks feasible in resource-constrained environments (such as mobile devices), it is beneficial to quantize models by using low-precision weights.
One common technique for quantizing neural networks is the straight-through gradient method, which enables back-propagation through the quantization mapping.
Despite its empirical success, little is understood about why the straight-through gradient method works.
Building upon a novel observation that the straight-through gradient method is in fact identical to the well-known Nesterov’s dual-averaging algorithm on a quantization constrained optimization problem, we propose a more principled alternative approach, called ProxQuant , that formulates quantized network training as a regularized learning problem instead and optimizes it via the prox-gradient method.
ProxQuant does back-propagation on the underlying full-precision vector and applies an efficient prox-operator in between stochastic gradient steps to encourage quantizedness.
For quantizing ResNets and LSTMs, ProxQuant outperforms state-of-the-art results on binary quantization and is on par with state-of-the-art on multi-bit quantization.
For binary quantization, our analysis shows both theoretically and experimentally that ProxQuant is more stable than the straight-through gradient method (i.e. BinaryConnect), challenging the indispensability of the straight-through gradient method and providing a powerful alternative.

assistant: text: A principled framework for model quantization using the proximal gradient method.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Deep Imitative Models for Flexible Inference, Planning, and Control

Paper content:
Imitation learning provides an appealing framework for autonomous control: in many tasks, demonstrations of preferred behavior can be readily obtained from human experts, removing the need for costly and potentially dangerous online data collection in the real world.
However, policies learned with imitation learning have limited flexibility to accommodate varied goals at test time.
Model-based reinforcement learning (MBRL) offers considerably more flexibility, since a predictive model learned from data can be used to achieve various goals at test time.
However, MBRL suffers from two shortcomings.
First, the model does not help to choose desired or safe outcomes -- its dynamics estimate only what is possible, not what is preferred.
Second, MBRL typically requires additional online data collection to ensure that the model is accurate in those situations that are actually encountered when attempting to achieve test time goals.
Collecting this data with a partially trained model can be dangerous and time-consuming.
In this paper, we aim to combine the benefits of imitation learning and MBRL, and propose imitative models: probabilistic predictive models able to plan expert-like trajectories to achieve arbitrary goals.
We find this method substantially outperforms both direct imitation and MBRL in a simulated autonomous driving task, and can be learned efficiently from a fixed set of expert demonstrations without additional online data collection.
We also show our model can flexibly incorporate user-supplied costs at test-time, can plan to sequences of goals, and can even perform well with imprecise goals, including goals on the wrong side of the road.

assistant: text: Hybrid Vision-Driven Imitation Learning and Model-Based Reinforcement Learning for Planning, Forecasting, and Control
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Regularizing Predictions via Class-wise Self-knowledge Distillation

Paper content:
Deep neural networks with millions of parameters may suffer from poor generalizations due to overfitting.
To mitigate the issue, we propose a new regularization method that penalizes the predictive distribution between similar samples.
In particular, we distill the predictive distribution between different samples of the same label and augmented samples of the same source during training.
In other words, we regularize the dark knowledge (i.e., the knowledge on wrong predictions) of a single network, i.e., a self-knowledge distillation technique, to force it output more meaningful predictions.
We demonstrate the effectiveness of the proposed method  via  experiments  on  various  image  classification  tasks:  it  improves  not only the generalization ability, but also the calibration accuracy of modern neural networks.

assistant: text: We propose a new regularization technique based on the knowledge distillation.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Discrete Structural Planning for Neural Machine Translation

Paper content:
Structural planning is important for producing long sentences, which is a missing part in current language generation models.
In this work, we add a planning phase in neural machine translation to control the coarse structure of output sentences.
The model first generates some planner codes, then predicts real output words conditioned on them.
The codes are learned to capture the coarse structure of the target sentence.
In order to learn the codes, we design an end-to-end neural network with a discretization bottleneck, which predicts the simplified part-of-speech tags of target sentences.
Experiments show that the translation performance are generally improved by planning ahead.
We also find that translations with different structures can be obtained by manipulating the planner codes.

assistant: text: Plan the syntactic structural of translation using codes
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Information asymmetry in KL-regularized RL

Paper content:
Many real world tasks exhibit rich structure that is repeated across different parts of the state space or in time.
In this work we study the possibility of leveraging such repeated structure to speed up and regularize learning.
We start from the KL regularized expected reward objective which introduces an additional component, a default policy.
Instead of relying on a fixed default policy, we learn it from data.
But crucially, we restrict the amount of information the default policy receives, forcing it to learn reusable behaviors that help the policy learn faster.
We formalize this strategy and discuss connections to information bottleneck approaches and to the variational EM algorithm.
We present empirical results in both discrete and continuous action domains and demonstrate that, for certain tasks, learning a default policy alongside the policy can significantly speed up and improve learning.
Please watch the video demonstrating learned experts and default policies on several continuous control tasks ( https://youtu.be/U2qA3llzus8 ).

assistant: text: Limiting state information for the default policy can improvement performance, in a KL-regularized RL framework where both agent and default policy are optimized together
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: The power of deeper networks for expressing natural functions

Paper content:
It is well-known that neural networks are universal approximators, but that deeper networks tend in practice to be more powerful than shallower ones.
We shed light on this by proving that the total number of neurons m required to approximate natural classes of multivariate polynomials of n variables grows only linearly with n for deep neural networks, but grows exponentially when merely a single hidden layer is allowed.
We also provide evidence that when the number of hidden layers is increased from 1 to k, the neuron requirement grows exponentially not with n but with n^{1/k}, suggesting that the minimum number of layers required for practical expressibility grows only logarithmically with n.

assistant: text: We prove that deep neural networks are exponentially more efficient than shallow ones at approximating sparse multivariate polynomials.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: PatchVAE: Learning Local Latent Codes for Recognition

Paper content:
Unsupervised representation learning holds the promise of exploiting large amount of available unlabeled data to learn general representations.
A promising technique for unsupervised learning is the framework of Variational Auto-encoders (VAEs).
However, unsupervised representations learned by VAEs are significantly outperformed by those learned by supervising for recognition.
Our hypothesis is that to learn useful representations for recognition the model needs to be encouraged to learn about repeating and consistent patterns in data.
Drawing inspiration from the mid-level representation discovery work, we propose PatchVAE, that reasons about images at patch level.
Our key contribution is a bottleneck formulation in a VAE framework that encourages mid-level style representations.
Our experiments demonstrate that representations learned by our method perform much better on the recognition tasks compared to those learned by vanilla VAEs.

assistant: text: A patch-based bottleneck formulation in a VAE framework that learns unsupervised representations better suited for visual recognition.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Causally Correct Partial Models for Reinforcement Learning

Paper content:
In reinforcement learning, we can learn a model of future observations and rewards, and use it to plan the agent's next actions.
However, jointly modeling future observations can be computationally expensive or even intractable if the observations are high-dimensional (e.g. images).
For this reason, previous works have considered partial models, which model only part of the observation.
In this paper, we show that partial models can be causally incorrect: they are confounded by the observations they don't model, and can therefore lead to incorrect planning.
To address this, we introduce a general family of partial models that are provably causally correct, but avoid the need to fully model future observations.

assistant: text: Causally correct partial models do not have to generate the whole observation to remain causally correct in stochastic environments.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Online Meta-Learning

Paper content:
A central capability of intelligent systems is the ability to continuously build upon previous experiences to speed up and enhance learning of new tasks.
Two distinct research paradigms have studied this question.
Meta-learning views this problem as learning a prior over model parameters that is amenable for fast adaptation on a new task, but typically assumes the set of tasks are available together as a batch.
In contrast, online (regret based) learning considers a sequential setting in which problems are revealed one after the other, but conventionally train only a single model without any task-specific adaptation.
This work introduces an online meta-learning setting, which merges ideas from both the aforementioned paradigms to better capture the spirit and practice of continual lifelong learning.
We propose the follow the meta leader (FTML) algorithm which extends the MAML algorithm to this setting.
Theoretically, this work provides an O(logT) regret guarantee for the FTML algorithm.
Our experimental evaluation on three different large-scale tasks suggest that the proposed algorithm significantly outperforms alternatives based on traditional online learning approaches.

assistant: text: We introduce the online meta learning problem setting to better capture the spirit and practice of continual lifelong learning.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Multi-Domain Processing via Hybrid Denoising Networks for Speech Enhancement

Paper content:
We present a hybrid framework that leverages the trade-off between temporal and frequency precision in audio representations to improve the performance of speech enhancement task.
We first show that conventional approaches using specific representations such as raw-audio and spectrograms are each effective at targeting different types of noise.
By integrating both approaches, our model can learn multi-scale and multi-domain features, effectively removing noise existing on different regions on the time-frequency space in a complementary way.
Experimental results show that the proposed hybrid model yields better performance and robustness than using each model individually.

assistant: text: A hybrid model utilizing both raw-audio and spectrogram information for speech enhancement tasks.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Analytical Moment Regularizer for Training Robust Networks

Paper content:
Despite the impressive performance of deep neural networks (DNNs) on numerous learning  tasks, they still exhibit uncouth behaviours.
One puzzling behaviour is the subtle sensitive reaction of DNNs to various noise attacks.
Such a nuisance has strengthened the line of research around developing and training noise-robust networks.
In this work, we propose a new training regularizer that aims to minimize the probabilistic expected training loss of a DNN subject to a generic Gaussian input.
We provide an efficient and simple approach to approximate such a regularizer for arbitrarily deep networks.
This is done by leveraging the analytic expression of the output mean of a shallow neural network, avoiding the need for memory and computation expensive data augmentation.
We conduct extensive experiments on LeNet and AlexNet on various datasets including MNIST, CIFAR10, and CIFAR100 to demonstrate the effectiveness of our proposed regularizer.
In particular, we show that networks that are trained with the proposed regularizer benefit from a boost in robustness against Gaussian noise to an equivalent amount of performing 3-21 folds of noisy data augmentation.
Moreover, we empirically show on several architectures and datasets that improving robustness against Gaussian noise, by using the new regularizer, can improve the overall robustness against 6 other types of attacks by two orders of magnitude.

assistant: text: An efficient estimate to the Gaussian first moment of DNNs as a regularizer to training robust networks.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: LABEL-EFFICIENT AUDIO CLASSIFICATION THROUGH MULTITASK LEARNING AND SELF-SUPERVISION

Paper content:
While deep learning has been incredibly successful in modeling tasks with large, carefully curated labeled datasets, its application to problems with limited labeled data remains a challenge.
The aim of the present work is to improve the label efficiency of large neural networks operating on audio data through a combination of multitask learning and self-supervised learning on unlabeled data.
We trained an end-to-end audio feature extractor based on WaveNet that feeds into simple, yet versatile task-specific neural networks.
We describe several easily implemented self-supervised learning tasks that can operate on any large, unlabeled audio corpus.
We demonstrate that, in scenarios with limited labeled training data, one can significantly improve the performance of three different supervised classification tasks individually by up to 6% through simultaneous training with these additional self-supervised tasks.
We also show that incorporating data augmentation into our multitask setting leads to even further gains in performance.

assistant: text: Label-efficient audio classification via multi-task learning and self-supervision
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Extreme Language Model Compression with Optimal Subwords and Shared Projections

Paper content:
Pre-trained deep neural network language models such as ELMo, GPT, BERT and XLNet have recently achieved state-of-the-art performance on a variety of language understanding tasks.
However, their size makes them impractical for a number of scenarios, especially on mobile and edge devices.
In particular, the input word embedding matrix accounts for a significant proportion of the model's memory footprint, due to the large input vocabulary and embedding dimensions.
Knowledge distillation techniques have had success at compressing large neural network models, but they are ineffective at yielding student models with vocabularies different from the original teacher models.
We introduce a novel knowledge distillation technique for training a student model with a significantly smaller vocabulary as well as lower embedding and hidden state dimensions.
Specifically, we employ a dual-training mechanism that trains the teacher and student models simultaneously to obtain optimal word embeddings for the student vocabulary.
We combine this approach with learning shared projection matrices that transfer layer-wise knowledge from the teacher model to the student model.
Our method is able to compress the BERT-BASE model by more than 60x, with only a minor drop in downstream task metrics, resulting in a language model with a footprint of under 7MB.
Experimental results also demonstrate higher compression efficiency and accuracy when compared with other state-of-the-art compression techniques.

assistant: text: We present novel distillation techniques that enable training student models with different vocabularies and compress BERT by 60x with minor performance drop.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Neural network gradient-based learning of black-box function interfaces

Paper content:
Deep neural networks work well at approximating complicated functions when provided with data and trained by gradient descent methods.
At the same time, there is a vast amount of existing functions that programmatically solve different tasks in a precise manner eliminating the need for training.
In many cases, it is possible to decompose a task to a series of functions, of which for some we may prefer to use a neural network to learn the functionality, while for others the preferred method would be to use existing black-box functions.
We propose a method for end-to-end training of a base neural network that integrates calls to existing black-box functions.
We do so by approximating the black-box functionality with a differentiable neural network in a way that drives the base network to comply with the black-box function interface during the end-to-end optimization process.
At inference time, we replace the differentiable estimator with its external black-box non-differentiable counterpart such that the base network output matches the input arguments of the black-box function.
Using this ``Estimate and Replace'' paradigm, we train a neural network, end to end, to compute the input to black-box functionality while eliminating the need for intermediate labels.
We show that by leveraging the existing precise black-box function during inference, the integrated model generalizes better than a fully differentiable model, and learns more efficiently compared to RL-based methods.

assistant: text: Training DNNs to interface w\ black box functions w\o intermediate labels by using an estimator sub-network that can be replaced with the black box after training
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: The Role of Embedding Complexity in Domain-invariant Representations

Paper content:
Unsupervised domain adaptation aims to generalize the hypothesis trained in a source domain to an unlabeled target domain.
One popular approach to this problem is to learn a domain-invariant representation for both domains.
In this work, we study, theoretically and empirically, the explicit effect of the embedding on generalization to the target domain.
In particular, the complexity of the class of embeddings affects an upper bound on the target domain's risk.
This is reflected in our experiments, too.

assistant: text: A general upper bound on the target domain's risk that reflects the role of embedding-complexity.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: VUSFA:Variational Universal Successor Features Approximator 

Paper content:
In this paper, we show how novel transfer reinforcement learning techniques can be applied to the complex task of target-driven navigation using the photorealisticAI2THOR simulator.
Specifically, we build on the concept of Universal SuccessorFeatures with an A3C agent.
We introduce the novel architectural1contribution of a Successor Feature Dependent Policy (SFDP) and adopt the concept of VariationalInformation Bottlenecks to achieve state of the art performance.VUSFA, our final architecture, is a straightforward approach that can be implemented using our open source repository.
Our approach is generalizable, showed greater stability in training, and outperformed recent approaches in terms of transfer learning ability.

assistant: text: We present an improved version of Universal Successor Features based DRL method which can improve the transfer learning of agents.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Data-driven construction of robust motion primitives for non-holonomic vehicles

Paper content:
We present a data driven approach to construct a library of feedback motion primitives for non-holonomic vehicles that guarantees bounded error in following arbitrarily long trajectories.
This ensures that motion re-planning can be avoided as long as disturbances to the vehicle remain within a certain bound and also potentially when the obstacles are displaced within a certain bound.
The library is constructed along local abstractions of the dynamics that enables addition of new motion primitives through abstraction refinement.
We provide sufficient conditions for construction of such robust motion primitives for a large class of nonlinear dynamics, including commonly used models, such as the standard Reeds-Shepp model.
The algorithm is applied for motion planning and control of a rover with slipping without its prior modelling.

assistant: text: We show that under some assumptions on vehicle dynamics and environment uncertainty it is possible to automatically synthesize motion primitives that do not accumulate error over time.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Discovering Motor Programs by Recomposing Demonstrations

Paper content:
In this paper, we present an approach to learn recomposable motor primitives across large-scale and diverse manipulation demonstrations.
Current approaches to decomposing demonstrations into primitives often assume manually defined primitives and bypass the difficulty of discovering these primitives.
On the other hand, approaches in primitive discovery put restrictive assumptions on the complexity of a primitive, which limit applicability to narrow tasks.
Our approach attempts to circumvent these challenges by jointly learning both the underlying motor primitives and recomposing these primitives to form the original demonstration.
Through constraints on both the parsimony of primitive decomposition and the simplicity of a given primitive, we are able to learn a diverse set of motor primitives, as well as a coherent latent representation for these primitives.
We demonstrate, both qualitatively and quantitatively, that our learned primitives capture semantically meaningful aspects of a demonstration.
This allows us to compose these primitives in a hierarchical reinforcement learning setup to efficiently solve robotic manipulation tasks like reaching and pushing.

assistant: text: We learn a space of motor primitives from unannotated robot demonstrations, and show these primitives are semantically meaningful and can be composed for new robot tasks.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Adversarial Attacks on Node Embeddings

Paper content:
The goal of network representation learning is to learn low-dimensional node embeddings that capture the graph structure and are useful for solving downstream tasks.
However, despite the proliferation of such methods there is currently no study of their robustness to adversarial attacks.
We provide the first adversarial vulnerability analysis on the widely used family of methods based on random walks.
We derive efficient adversarial perturbations that poison the network structure and have a negative effect on both the quality of the embeddings and the downstream tasks.
We further show that our attacks are transferable since they generalize to many models, and are successful even when the attacker is restricted.

assistant: text: Adversarial attacks on unsupervised node embeddings based on eigenvalue perturbation theory.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Simple but effective techniques to reduce dataset biases

Paper content:
There have been several studies recently showing that strong natural language understanding (NLU) models are prone to relying on unwanted dataset biases without learning the underlying task, resulting in models which fail to generalize to out-of-domain datasets, and are likely to perform poorly in real-world scenarios.
We propose several learning strategies to train neural models which are more robust to such biases and transfer better to out-of-domain datasets.
We introduce an additional lightweight bias-only model which learns dataset biases and uses its prediction to adjust the loss of the base model to reduce the biases.
In other words, our methods down-weight the importance of the biased examples, and focus training on hard examples, i.e. examples that cannot be correctly classified by only relying on biases.
Our approaches are model agnostic and simple to implement.
We experiment on large-scale natural language inference and fact verification datasets and their out-of-domain datasets and show that our debiased models significantly improve the robustness in all settings, including gaining 9.76 points on the FEVER symmetric evaluation dataset, 5.45 on the HANS dataset and 4.78 points on the SNLI hard set.
These datasets are specifically designed to assess the robustness of models in the out-of-domain setting where typical biases in the training data do not exist in the evaluation set.

assistant: text: We propose several general debiasing strategies to address common biases seen in different datasets and obtain substantial improved out-of-domain performance in all settings.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Knowledge Flow: Improve Upon Your Teachers

Paper content:
A zoo of deep nets is available these days for almost any given task, and it is increasingly unclear which net to start with when addressing a new task, or which net to use as an initialization for fine-tuning a new model.
To address this issue, in this paper, we develop knowledge flow which moves ‘knowledge’ from multiple deep nets, referred to as teachers, to a new deep net model, called the student.
The structure of the teachers and the student can differ arbitrarily and they can be trained on entirely different tasks with different output spaces too.
Upon training with knowledge flow the student is independent of the teachers.
We demonstrate our approach on a variety of supervised and reinforcement learning tasks, outperforming fine-tuning and other ‘knowledge exchange’ methods.

assistant: text: ‘Knowledge Flow’ trains a deep net (student) by injecting information from multiple nets (teachers). The student is independent upon training and performs very well on learned tasks irrespective of the setting (reinforcement or supervised learning).
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Learning Particle Dynamics for Manipulating Rigid Bodies, Deformable Objects, and Fluids

Paper content:
Real-life control tasks involve matters of various substances---rigid or soft bodies, liquid, gas---each with distinct physical behaviors.
This poses challenges to traditional rigid-body physics engines.
Particle-based simulators have been developed to model the dynamics of these complex scenes; however, relying on approximation techniques, their simulation often deviates from real-world physics, especially in the long term.
In this paper, we propose to learn a particle-based simulator for complex control tasks.
Combining learning with particle-based systems brings in two major benefits: first, the learned simulator, just like other particle-based systems, acts widely on objects of different materials; second, the particle-based representation poses strong inductive bias for learning: particles of the same type have the same dynamics within.
This enables the model to quickly adapt to new environments of unknown dynamics within a few observations.
We demonstrate robots achieving complex manipulation tasks using the learned simulator, such as manipulating fluids and deformable foam, with experiments both in simulation and in the real world.
Our study helps lay the foundation for robot learning of dynamic scenes with particle-based representations.

assistant: text: Learning particle dynamics with dynamic interaction graphs for simulating and control rigid bodies, deformable objects, and fluids.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Learning and Planning with a Semantic Model

Paper content:
Building deep reinforcement learning agents that can generalize and adapt to unseen environments remains a fundamental challenge for AI.
This paper describes progresses on this challenge in the context of man-made environments, which are visually diverse but contain intrinsic semantic regularities.
We propose a hybrid model-based and model-free approach, LEArning and Planning with Semantics (LEAPS), consisting of a multi-target sub-policy that acts on visual inputs, and a Bayesian model over semantic structures.
When placed in an unseen environment, the agent plans with the semantic model to make high-level decisions, proposes the next sub-target for the sub-policy to execute, and updates the semantic model based on new observations.
We perform experiments in visual navigation tasks using House3D, a 3D environment that contains diverse human-designed indoor scenes with real-world objects.
LEAPS outperforms strong baselines that do not explicitly plan using the semantic content.

assistant: text: We propose a hybrid model-based & model-free approach using semantic information to improve DRL generalization in man-made environments.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Deep Active Learning for Named Entity Recognition

Paper content:
Deep learning has yielded state-of-the-art performance on many natural language processing tasks including named entity recognition (NER).
However, this typically requires large amounts of labeled data.
In this work, we demonstrate that the amount of labeled training data can be drastically reduced when deep learning is combined with active learning.
While active learning is sample-efficient, it can be computationally expensive since it requires iterative retraining.
To speed this up, we introduce a lightweight architecture for NER, viz., the CNN-CNN-LSTM model consisting of convolutional character and word encoders and a  long short term memory (LSTM) tag decoder.
The model achieves nearly state-of-the-art performance on standard datasets for the task while being computationally much more efficient than best performing models.
We carry out incremental active learning, during the training process, and are able to nearly match state-of-the-art performance with just 25\% of the original training data.

assistant: text: We introduce a lightweight architecture for named entity recognition and carry out incremental active learning, which is able to match state-of-the-art performance with just 25% of the original training data.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Hindsight policy gradients

Paper content:
A reinforcement learning agent that needs to pursue different goals across episodes requires a goal-conditional policy.
In addition to their potential to generalize desirable behavior to unseen goals, such policies may also enable higher-level planning based on subgoals.
In sparse-reward environments, the capacity to exploit information about the degree to which an arbitrary goal has been achieved while another goal was intended appears crucial to enable sample efficient learning.
However, reinforcement learning agents have only recently been endowed with such capacity for hindsight.
In this paper, we demonstrate how hindsight can be introduced to policy gradient methods, generalizing this idea to a broad class of successful algorithms.
Our experiments on a diverse selection of sparse-reward environments show that hindsight leads to a remarkable increase in sample efficiency.

assistant: text: We introduce the capacity to exploit information about the degree to which an arbitrary goal has been achieved while another goal was intended to policy gradient methods.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Random mesh projectors for inverse problems

Paper content:
We propose a new learning-based approach to solve ill-posed inverse problems in imaging.
We address the case where ground truth training samples are rare and the problem is severely ill-posed---both because of the underlying physics and because we can only get few measurements.
This setting is common in geophysical imaging and remote sensing.
We show that in this case the common approach to directly learn the mapping from the measured data to the reconstruction becomes unstable.
Instead, we propose to first learn an ensemble of simpler mappings from the data to projections of the unknown image into random piecewise-constant subspaces.
We then combine the projections to form a final reconstruction by solving a deconvolution-like problem.
We show experimentally that the proposed method is more robust to measurement noise and corruptions not seen during training than a directly learned inverse.

assistant: text: We solve ill-posed inverse problems with scarce ground truth examples by estimating an ensemble of random projections of the model instead of the model itself.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: NOODL: Provable Online Dictionary Learning and Sparse Coding

Paper content:
We consider the dictionary learning problem, where the aim is to model the given data as a linear combination of a few columns of a matrix known as a dictionary, where the sparse weights forming the linear combination are known as coefficients.
Since the dictionary and coefficients, parameterizing the linear model are unknown, the corresponding optimization is inherently non-convex.
This was a major challenge until recently, when provable algorithms for dictionary learning were proposed.
Yet, these provide guarantees only on the recovery of the dictionary, without explicit recovery guarantees on the coefficients.
Moreover, any estimation error in the dictionary adversely impacts the ability to successfully localize and estimate the coefficients.
This potentially limits the utility of existing provable dictionary learning methods in applications where coefficient recovery is of interest.
To this end, we develop NOODL: a simple Neurally plausible alternating Optimization-based Online Dictionary Learning algorithm, which recovers both the dictionary and coefficients exactly at a geometric rate, when initialized appropriately.
Our algorithm, NOODL, is also scalable and amenable for large scale distributed implementations in neural architectures, by which we mean that it only involves simple linear and non-linear operations.
Finally, we corroborate these theoretical results via experimental evaluation of the proposed algorithm with the current state-of-the-art techniques.

assistant: text: We present a provable algorithm for exactly recovering both factors of the dictionary learning model.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Distinguishability of Adversarial Examples

Paper content:
Machine learning models including traditional models and neural networks can be easily fooled by adversarial examples which are generated from the natural examples with small perturbations.
This poses a critical challenge to machine learning security, and impedes the wide application of machine learning in many important domains such as computer vision and malware detection.
Unfortunately, even state-of-the-art defense approaches such as adversarial training and defensive distillation still suffer from major limitations and can be circumvented.
From a unique angle, we propose to investigate two important research questions in this paper: Are adversarial examples distinguishable from natural examples?
Are adversarial examples generated by different methods distinguishable from each other?
These two questions concern the distinguishability of adversarial examples.
Answering them will potentially lead to a simple yet effective approach, termed as defensive distinction in this paper under the formulation of multi-label classification, for protecting against adversarial examples.
We design and perform experiments using the MNIST dataset to investigate these two questions, and obtain highly positive results demonstrating the strong distinguishability of adversarial examples.
We recommend that this unique defensive distinction approach should be seriously considered to complement other defense approaches.

assistant: text: We propose a defensive distinction protection approach and demonstrate the strong distinguishability of adversarial examples.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Coordinate-VAE: Unsupervised clustering and de-noising of peripheral nervous system data

Paper content:
The peripheral nervous system represents the input/output system for the brain.
Cuff electrodes implanted on the peripheral nervous system allow observation and control over this system, however, the data produced by these electrodes have a low signal-to-noise ratio and a complex signal content.
In this paper, we consider the analysis of neural data recorded from the vagus nerve in animal models, and develop an unsupervised learner based on convolutional neural networks that is able to simultaneously de-noise and cluster regions of the data by signal content.

assistant: text: Unsupervised analysis of data recorded from the peripheral nervous system denoises and categorises signals.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Towards Stable and Efficient Training of Verifiably Robust Neural Networks

Paper content:
Training neural networks with verifiable robustness guarantees is challenging.
Several existing approaches utilize linear relaxation based neural network output bounds under perturbation, but they can slow down training by a factor of hundreds depending on the underlying network architectures.
Meanwhile, interval bound propagation (IBP) based training is efficient and significantly outperforms linear relaxation based methods on many tasks, yet it may suffer from stability issues since the bounds are much looser especially at the beginning of training.
In this paper, we propose a new certified adversarial training method, CROWN-IBP, by combining the fast IBP bounds in a forward bounding pass and a tight linear relaxation based bound, CROWN, in a backward bounding pass.
CROWN-IBP is computationally efficient and consistently outperforms IBP baselines on training verifiably robust neural networks.
We conduct large scale experiments on MNIST and CIFAR datasets, and outperform all previous linear relaxation and bound propagation based certified defenses in L_inf robustness.
Notably, we achieve 7.02% verified test error on MNIST at epsilon=0.3, and 66.94% on CIFAR-10 with epsilon=8/255.

assistant: text: We propose a new certified adversarial training method, CROWN-IBP, that achieves state-of-the-art robustness for L_inf norm adversarial perturbations.