pwc-archive/papers-with-abstracts huggingface dataset:
Meta-learning as Learning the Meta: A Videogame-Theoretic Perspective on\\ Learning to Learn
Authors: Anonymous
Date: 2021-03-13 00:00:00
Proceeding: ICLR Workshop Learning_to_Learn 2021 5
Tasks: Meta-Learning

The field of meta-learning involves, not the training of a model for a particular task, but using training on a variety of related tasks to develop a model that transfers readily and generally to any of a set of similar tasks from some distribution. Similarly, top players of competitive videogames are required to contend with frequent updates or patches to their game of choice, and are expected to adapt to the changes quickly and without a significant decrease in skill. By considering each patch of a competitive game to be a separate task, drawn from a distribution describing versions thereof, the adaptation of players to new patches becomes analogous to the adaptation of models to new tasks. This paper seeks to describe the process of players adapting to updates of their games in the language of meta-learning, and then to analyze that process to inspire future modifications to the meta-learning paradigm.
FEW-SHOTLEARNING WITH WEAK SUPERVISION
Authors: Ali Ghadirzadeh, Petra Poklukar, Xi Chen, Huaxiu Yao, Hossein Azizpour, Mårten Björkman, Chelsea Finn, Danica Kragic
Date: 2021-03-13 00:00:00
Proceeding: ICLR Workshop Learning_to_Learn 2021 5
Tasks: Meta-Learning, Variational Inference

Few-shot meta-learning methods aim to learn the common structure shared across a set of tasks to facilitate learning  new tasks with small amounts of data. However, provided only a few training examples, many tasks are ambiguous. Such ambiguity can be mitigated with side information in terms of weak labels which is often readily available. In this paper, we propose a Bayesian gradient-based meta-learning algorithm that can incorporate weak labels to reduce task ambiguity and improve performance. Our approach is cast in the framework of amortized variational inference and trained by optimizing a variational lower bound. The proposed method is competitive to state-of-the-art methods and achieves significant performance gains in settings where weak labels are available.
Neurons learn slower than they think
Authors: Anonymous
Date: 2021-03-13 00:00:00
Proceeding: ICLR Workshop Learning_to_Learn 2021 5

Recent studies revealed complex convergence dynamics in gradient-based methods, which has been little understood so far. Changing the step size to balance between high convergence rate and small generalization error may not be sufficient: maximizing the test accuracy usually requires a larger learning rate than minimizing the training loss. To explore the dynamic bounds of convergence rate, this study introduces \textit{differential capability} into an optimization process, which measures whether the test accuracy increases as fast as a model approaches the decision boundary in a classification problem. The convergence analysis showed that: 1) a higher convergence rate leads to slower capability growth; 2) a lower convergence rate results in faster capability growth and decay; 3) regulating a convergence rate in either direction reduces differential capability.
Trustworthy Disease Discovery: A Case Study in Alzheimer’s
Authors: Anonymous
Date: 2021-03-13 00:00:00
Proceeding: ICLR Workshop Learning_to_Learn 2021 5
Tasks: Feature Importance

A small number of high-dimensional training samples is a challenging problem for understanding the dynamics of multivariate spatiotemporal neuroimaging.  Often, reducing these dynamics to a small number of handcrafted features helps. For example, the matrix of Pearson's correlation coefficients is highly predictive.  Nevertheless, it is hard to perceive the dynamics and the disorder from these compressed proxy representations.  In this paper, we propose a hierarchical recurrent model with attention that learns dynamics directly from temporal signals and captures stable interpretations of abnormal conditions predictive of disorder under consideration.  We study these abnormalities in dynamics through feature importance estimation in the resting-state functional MRI data using different interpretability methods.  We validate this feature estimation by introducing the Retain and Retrain (RAR) process and demonstrate its utility on an Alzheimer's disease dataset.  Furthermore, we show that the proposed model is adaptable to small sample case by offering a self-supervised pretraining scheme of the same model. With this scheme, we demonstrate that the model can leverage a large unrelated but publicly available dataset to learn improved representation to maintain adequate predictive capacity and extract useful disorder-specific information.
Meta Learning for Multi-agent Communication
Authors: Abhinav Gupta, Angeliki Lazaridou, Marc Lanctot
Date: 2021-03-13 00:00:00
Proceeding: ICLR Workshop Learning_to_Learn 2021 5
Tasks: Diversity, Meta-Learning, Meta Reinforcement Learning

Recent works have shown remarkable progress in training artificial agents to understand natural language but are focused on using large amounts of raw data involving huge compute requirements. An interesting hypothesis follows the idea of training artificial agents via multi-agent communication while using small amounts of task-specific human data to ground the emergent language into natural language. This allows agents to communicate with humans without needing enormous expensive human demonstrations. Evolutionary studies have showed that simpler and easily adaptable languages arise as a result of communicating with a diverse group of large population. We propose to model this supposition with artificial agents and propose an adaptive population-based meta-reinforcement learning approach that builds such a population in an iterative manner. We show empirical results on referential games involving natural language where our agents outperform all baselines on both the task performance and language score including human evaluation. We demonstrate that our method induces constructive diversity into a growing population of agents that is beneficial in training the meta-agent.
Meta-Learning for Planning: Automatic Synthesis of Sample Based Planners
Authors: Lucas Paul Saldyt, Heni Amor
Date: 2021-03-13 00:00:00
Proceeding: ICLR Workshop Learning_to_Learn 2021 5
Tasks: Decision Making, Meta-Learning, Sequential Decision Making, Starcraft

In this paper, we discuss the challenge of generating domain-specific path planners in a data-driven fashion. Via the multi-objective optimization of Python code, we synthesize new sampling-based path planners that allow robots to adapt to new tasks and environments involving sequential decision-making.  In addition to the ability  to  adapt  to  new  environments,  our  approach  also  enables  robots  to  balance their computational needs with improvements in task performance. We show that new computer programs can be generated which represent diverse variants of RRT* optimized to StarCraft maps.
Metalearning Using Structure-rich Pipeline Representations for Better AutoML
Authors: Anonymous
Date: 2021-03-13 00:00:00
Proceeding: ICLR Workshop Learning_to_Learn 2021 5
Tasks: AutoML, reinforcement-learning, Reinforcement Learning (RL), tabular-classification

Automatic machine learning (AutoML) systems have been shown to perform better when they learn from past experience. Examples include Auto-sklearn, which warm-starts the ML pipeline search using existing programs known to perform well on "similar" tasks, and AlphaD3M, which uses online reinforcement learning to search the ML pipeline space. These metalearning approaches, as well as many others, depend on simplifying assumptions about the pipeline search space and/or the pipeline representation. We show that we can estimate ML pipeline performance without simplifying the representation of the pipeline structure. We evaluate this approach on tabular classification tasks and find that it produces the best estimates of pipeline performance and yields pipeline rankings with the highest normalized discounted cumulative gain (nDCG) and the lowest regret.
Accelerating Online Reinforcement Learning via Model-Based Meta-Learning
Authors: John D Co-Reyes, Sarah Feng, Glen Berseth, Jie Qui, Sergey Levine
Date: 2021-03-15 00:00:00
Proceeding: ICLR Workshop Learning_to_Learn 2021 5
Tasks: Meta-Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Current reinforcement learning algorithms struggle to quickly adapt to new situations without  large amounts of experience and usually without large amounts of optimization over that experience. In this work we seek to leverage meta-learning methods from MAML with model-based RL methods based on MuZero to design agents which can quickly adapt online. We propose a new model-based meta-RL algorithm that can adapt online to new experience and can be meta-trained without explicit task labels. Compared to prior model-based meta-learning methods, our work can scale to more visually complex image based environments with dynamics that change significantly over time and can handle the continual RL setting which has no episodic boundaries.
Lower Bounding Rate-Distortion From Samples
Authors: Yibo Yang, Stephan Mandt
Date: 2021-03-04 00:00:00
Proceeding: ICLR Workshop Neural_Compression 2021 5
Tasks: Stochastic Optimization

The rate-distortion function tells us the minimal number of bits on average to compress a random object within a given distortion tolerance. A lower bound on the rate-distortion function therefore represents a fundamental limit on the best possible rate-distortion performance of any lossy compression algorithm, and can help us assess the potential room for improvement. We make a first attempt at an algorithm for computing such a lower bound, applicable to general data sources that we have samples of. Based on a dual characterization of the rate-distortion function in terms of a constrained maximization problem, our method approximates the exact constraint function by an asymptotically unbiased estimator, allowing for stochastic optimization. On a 2D Gaussian source, we obtain a lower bound within 1 bit of the analytical rate-distortion function.
Graph Autoencoder for Graph Compression and Representation Learning
Authors: Yunhao Ge, Yunkui Pang, Linwei Li, Laurent Itti
Date: 2021-03-04 00:00:00
Proceeding: ICLR Workshop Neural_Compression 2021 5
Tasks: Data Compression, Graph Classification, Graph Representation Learning, Representation Learning

We consider the problem of graph data compression and representation. Recent developments in graph neural networks (GNNs) focus on generalizing convolutional neural networks (CNNs) to graph data, which includes redesigning convolution and pooling operations for graphs. However, few methods focus on effective graph compression to obtain a smaller graph, which can reconstruct the original full graph with less storage and can provide useful latent representations to improve downstream task performance. To fill this gap, we propose Multi-kernel Inductive Attention Graph Autoencoder (MIAGAE), which, instead of compressing nodes/edges separately, utilizes the node similarity and graph structure to compress all nodes and edges as a whole. Similarity attention graph pooling selects the most representative nodes with the most information by using the similarity and topology among nodes. Our multi-kernel Inductive-Convolution layer can focus on different aspects and learn more general node representations in evolving graphs. We demonstrate that MIAGAE outperforms state-of-the-art methods for graph compression and few-shot graph classification, with superior graph representation learning.
Lossless Compression and Generalization in Overparameterized Models: The Case of Boosting
Authors: Nikolaos Nikolaou
Date: 2021-03-04 00:00:00
Proceeding: ICLR Workshop Neural_Compression 2021 5
Tasks: Ensemble Learning

Successful learning algorithms like DNNs, kernel methods or ensemble learning methods, have been known to produce models that exhibit good generalization  despite being drawn  from  overparameterized  model  families. This observation has put in question the  convex relationship between model complexity and generalization. We instead propose rethinking the relevant notion of model complexity for the purposes of assessing the complexity of models trained on a given dataset. Borrowing from information theory, we identify the optimal model one can train on a given dataset as one achieving its lossless maximal compression. In the noiseless dataset setting, it can be shown that such a model coincides with an average margin maximizer of the training data. Experimental results on gradient boosting confirm our observations and show that the minimal generalization error is attained in expectation by models achieving lossless maximal compression of the training data.
PAC-Bayes and Information Complexity
Authors: Pradeep Kr. Banerjee, Guido Montufar
Date: 2021-03-04 00:00:00
Proceeding: ICLR Workshop Neural_Compression 2021 5
Tasks: Generalization Bounds

We point out that a number of well-known PAC-Bayesian-style and information-theoretic generalization bounds for randomized learning algorithms can be derived under a common framework starting from a fundamental information exponential inequality. We also obtain new bounds for data-dependent priors and unbounded loss functions. Optimizing these bounds naturally gives rise to a method called Information Complexity Minimization for which we discuss two practical examples for learning with neural networks, namely Entropy- and PAC-Bayes- SGD.
BAYESIAN COMPRESSED DEEP LEARNING FOR STATE ESTIMATION OF UNOBSERVABLE POWER SYSTEMS
Authors: Rafael Lima
Date: 2021-03-04 00:00:00
Proceeding: ICLR Workshop Neural_Compression 2021 5
Tasks: Deep Learning, State Estimation

In recent years, state-of-the-art Deep Learning (DL)-based modeling has been applied to the problem of state estimation of unobservable electrical distribution systems, with promising results. Unfortunately, the definition and training of these flexible models have been largely heuristic, which may result in oversized neural networks, with computationally inefficient layers. In this work, we apply the method of Bayesian Compression for eliminating spurious redundancies of DL-based State Estimation models. Experimental results in four test networks, including two IEEE Test Case Power Networks, corroborate the benefits of the proposed compression approach for obtaining reduced versions of the models without compromising their performance.
Importance weighted compression
Authors: Lucas Theis, Jonathan Ho
Date: 2021-03-04 00:00:00
Proceeding: ICLR Workshop Neural_Compression 2021 5

The connection between variational autoencoders (VAEs) and compression is well established and they have been used for both lossless and lossy compression. Compared to VAEs, importance-weighted autoencoders (IWAEs) achieve a larger bound on the log-likelihood. However, it is not well understood whether a similar connection between IWAEs and compression exists and whether the improved loss corresponds to better compression performance. Here we show that the loss of IWAEs can indeed be interpreted as the cost of lossless or lossy compression schemes, and using IWAEs for compression can lead to small improvements in performance.
Neural data compression for physics plasma simulation
Authors: Jong Choi, Michael Churchill, Qian Gong, Seung-Hoe Ku, Jaemoon Lee, Anand Rangarajan, Sanjay Ranka, Dave Pugmire, CS Chang, Scott Klasky
Date: 2021-03-04 00:00:00
Proceeding: ICLR Workshop Neural_Compression 2021 5
Tasks: Data Compression

We present a VAE-based data compression method, called VAe Physics Optimized Reduction (VAPOR), to compress scientific data while preserving physics constraints. VAPOR is based on Vector Quantized Variational Auto Encoder (VQ-VAE) and extended with physics-informed optimization functions and refinement layers, focusing on compressing and reconstructing scientific data with minimum loss of information under physics constraints. We demonstrate VAPOR by using outputs from XGC, a massively parallel fusion simulation code running on the largest supercomputers. Key features of VAPOR are three-fold; i) find a reduced representation of physics data, ii) reconstruct the data with a minimum loss, iii) preserve physics information (e.g., mass, energy, moment conservation).
We discuss challenges in XGC 5D data reconstruction and present our initial experiences and results on how we construct Deep Neural Network (DNN) of VAPOR to optimize the reconstruction quality of XGC data and integrate XGC’s physics constraints.
Hybrid Mutual Information Lower-bound Estimators for Representation Learning
Authors: Abhishek Sinha, Jiaming Song, Stefano Ermon
Date: 2021-03-04 00:00:00
Proceeding: ICLR Workshop Neural_Compression 2021 5
Tasks: Representation Learning

Self-supervised representation learning methods based on the principle of maximizing mutual information have been successful in unsupervised learning of visual representations. These approaches are low-variance mutual information lower bound estimators, yet the lack of distributional assumptions prevent them from learning certain important information such as texture. Estimators that are based on distributional assumptions bypass this issue with autoencoders but they tend to have worse performance on downstream classification. To this end, we consider a hybrid approach that incorporates both the distribution-free contrastive lower bound and the distribution-based autoencoder lower bound. We illustrate that with one set of representations, the hybrid approach is able to achieve good performance on multiple downstream tasks such as classification, reconstruction, and generation.
Hierarchical Image Compression Framework
Authors: Yunying Ge, Jing Wang, Yibo Shi, Shangyin Gao
Date: 2021-03-04 00:00:00
Proceeding: ICLR Workshop Neural_Compression 2021 5
Tasks: Image Compression

In learning-based image compression approaches, compression models are based on variational autoencoder(VAE) framework and optimized by a rate-distortion objective function, which achieve better performance than hybrid codecs. However, VAE maps the input to a lower dimensional latent space which becomes a bottleneck of reconstruction. In this paper, we propose a deep Hierarchical Compression(HC) model, which can achieve good compression performance from low-bit to very high-bit. HC model consists of two closely-related modules, including hierarchical latent compression module and Hierarchical Conditional Entropy(HCE) module. Such a design transmits the details in the shallower layers and coarse information in the deeper layers and conditions the shallower entropy estimation on the deeper information. Extensive experiments show that HC model could breakthrough the AE limit and achieve significant improvements over state-of-the-art approaches in the high quality regime.
On reducing the correlation of bottleneck representations in Autoencoders
Authors: Anonymous
Date: 2021-03-04 00:00:00
Tasks: Decoder, Image Compression

Image compression is an important image processing task. Recently, there has been more interest in using autoencoders (AEs) to solve this task. An AE has two goals: (i) compress the original input to a low-dimensional space, at the bottleneck of the network topology, using the encoder (ii) reconstruct the input from the representation at the bottleneck using the decoder. Both parts are optimized jointly by minimizing a distortion-based loss which implicitly forces the model to keep only the variations in the input data required to reconstruct the input without persevering the redundancies. In this paper, we propose a scheme to explicitly penalize feature redundancies in the bottleneck representation. To this end, we propose an additional loss term, based on the pair-wise correlation of the neurons, which complements the standard reconstruction loss forcing the encoder to learn a more diverse and richer representation of the input. The proposed approach is tested using the MNIST dataset and leads to superior experimental results.
Diversity Based Edge Pruning of Neural Networks Using Determinantal Point Processes
Authors: Rupam Acharyya, Boyu Zhang, Ankani Chattoraj, Shouman Das, Daniel Stefankovic
Date: 2021-03-04 00:00:00
Proceeding: ICLR Workshop Neural_Compression 2021 5
Tasks: Diversity, Point Processes

Deep learning architectures with huge number of parameters are often compressed using pruning techniques. Two classes of pruning techniques are node pruning and edge pruning. A fairly recent work established that Determinantal Point Process (DPP) based node pruning empirically outperforms competing node pruning methods. However, one prominent appeal of edge pruning over node pruning is the consistent finding in literature is that sparse neural networks (edge pruned) generalize better than dense neural networks (node pruned). Building on these previous work and drawing motivation from synaptic diversity in the brain, we propose a novel diversity-based edge pruning technique for neural networks using DPP. We then empirically show that DPP edge pruning for neural networks outperforms other competing methods (both edge and node) on real data.
Understanding Diversity Based Neural Network Pruning in Teacher Student Setup
Authors: Rupam Acharyya, Ankani Chattoraj, Boyu Zhang, Shouman Das, Daniel Stefankovic
Date: 2021-03-04 00:00:00
Proceeding: ICLR Workshop Neural_Compression 2021 5
Tasks: Diversity, Network Pruning

Despite multitude of empirical advances, there is a lack of theoretical understanding of the effectiveness of different pruning methods. We inspect different pruning techniques under the statistical mechanics formulation of a teacher-student framework and derive their generalization error (GE) bounds. In the first part, we theoretically prove empirical observations of a recent work that showed Determinantal Point Process (DPP) based node pruning method is notably superior to competing approaches when tested on real datasets. In the second part, we use our theoretical setup to prove that the baseline random edge pruning method performs better than the DPP node pruning method, consistent with the finding in literature that sparse neural networks (edge pruned) generalize better than dense neural networks (node pruned) for a fixed number of parameters.
Data Compression with Bayesian Attention Networks
Authors: Anonymous
Date: 2021-03-04 00:00:00
Tasks: Data Compression

The lossless data compression algorithm based on Bayesian Attention Networks is derived from first principles.