paper_name stringlengths 11 170 | text stringlengths 8.07k 307k | summary stringlengths 152 6.16k | paper_id stringlengths 43 43 |
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For interpolating kernel machines, minimizing the norm of the ERM solution minimizes stability | 1 INTRODUCTION . Statistical learning theory studies the learning properties of machine learning algorithms , and more fundamentally , the conditions under which learning from finite data is possible . In this context , classical learning theory focuses on the size of the hypothesis space in terms of different complexi... | This paper investigates kernel ridge-less regression from a stability viewpoint by deriving its risk bounds. Using stability arguments to derive risk bounds have been widely adopting in machine learning. However, related studies on kernel ridge-less regression are still sparse. The present study fills this gap, which, ... | SP:4d08cdb2de2044bcb574a425b42963b83fbebfbc |
Discriminative Representation Loss (DRL): A More Efficient Approach than Gradient Re-Projection in Continual Learning | 1 INTRODUCTION . In the real world , we are often faced with situations where data distributions are changing over time , and we would like to update our models by new data in time , with bounded growth in system size . These situations fall under the umbrella of β continual learning β , which has many practical applic... | This paper presents a novel way of making full use of compact episodic memory to alleviate catastrophic forgetting in continual learning. This is done by adding the proposed discriminative representation loss to regularize the gradients produced by new samples. Authors gave insightful analysis on the influence of gradi... | SP:b80bc890180934092cde037b49d94d6e4e06fad9 |
Learning without Forgetting: Task Aware Multitask Learning for Multi-Modality Tasks | 1 INTRODUCTION . The process of Multi-Task Learning ( MTL ) on a set of related tasks is inspired by the patterns displayed by human learning . It involves a pretraining phase over all the tasks , followed by a finetuning phase . During pretraining , the model tries to grasp the shared knowledge of all the tasks involv... | This paper proposes a new framework that computes the task-specific representations to modulate the model parameters during the multi-task learning (MTL). This framework uses a single model with shared representations for learning multiple tasks together. Also, explicit task information may not be always available, in ... | SP:09f2fe6a482bbd6f9bd2c62aa841f995171ba939 |
A Robust Fuel Optimization Strategy For Hybrid Electric Vehicles: A Deep Reinforcement Learning Based Continuous Time Design Approach | 1 INTRODUCTION . Hybrid electric vehicles powered by fuel cells and batteries have attracted great enthusiasm in modern days as they have the potential to eliminate emissions from the transport sector . Now , both the fuel cells and batteries have got several operational challenges which make the separate use of each o... | This work proposes a deep reinforcement learning-based optimization strategy to the fuel optimization problem for the hybrid electric vehicle. The problem has been formulated as a fully observed stochastic Markov Decision Process (MDP). A deep neural network is used to parameterize the policy and value function. A cont... | SP:a1e2218e6943bf138aeb359e23628676b396ed66 |
Neural representation and generation for RNA secondary structures | 1 INTRODUCTION . There is an increasing interest in developing deep generative models for biochemical data , especially in the context of generating drug-like molecules . Learning generative models of biochemical molecules can facilitate the development and discovery of novel treatments for various diseases , reducing ... | This paper proposes 3 deep generative models based on VAEs (with different encoding schemes for RNA secondary structure) for the generation of RNA secondary structures. They test each model on 3 benchmark tasks: unsupervised generation, semi-supervised learning and targeted generation. This paper has many interesting ... | SP:43e525fb3fa611df7fd44bd3bc9843e57b154c66 |
DiP Benchmark Tests: Evaluation Benchmarks for Discourse Phenomena in MT | 1 INTRODUCTION AND RELATED WORK . The advances in neural machine translation ( NMT ) systems have led to great achievements in terms of state-of-the-art performance in automatic translation tasks . There have even been claims that their translations are no worse than what an average bilingual human may produce ( Wu et ... | This paper presents a benchmark for discourse phenomena in machine translation. Its main novelty lies in the relatively large scale, spanning three translation directions, four discourse phenomena, and 150-5000 data points per language and phenomenon. A relatively large number of systems from previous work is benchmark... | SP:0bd749fe44c37b521bd40f701e1428890aaa9c95 |
Private Image Reconstruction from System Side Channels Using Generative Models | 1 INTRODUCTION . Side channel analysis ( SCA ) recovers program secrets based on the victim program β s nonfunctional characteristics ( e.g. , its execution time ) that depend on the values of program secrets . SCA constitutes a major threat in today β s system and hardware security landscape . System side channels , s... | The authors present a framework that uses a combination of VAE and GAN to recover private user images using Side channel analysis of memory access . A VAE-LP model first reconstructs a coarse image from side channel information which is reshaped and processed using a convolutional network. The output of the VAE-LP mo... | SP:b2fc6ca65add04fb32bcf7622d9098de9004ca2b |
DICE: Diversity in Deep Ensembles via Conditional Redundancy Adversarial Estimation | Deep ensembles perform better than a single network thanks to the diversity among their members . Recent approaches regularize predictions to increase diversity ; however , they also drastically decrease individual members β performances . In this paper , we argue that learning strategies for deep ensembles need to tac... | This paper proposes a method of learning ensembles that adhere to an "ensemble version" of the information bottleneck principle. Whereas the information bottleneck principle says the representation should avoid spurious correlations between the representation (Z) and the training data (X) that is not useful for predict... | SP:7fb11c941e8d79248ce5ff7caa0535a466303395 |
Zero-shot Synthesis with Group-Supervised Learning | 1 INTRODUCTION . Primates perform well at generalization tasks . If presented with a single visual instance of an object , they often immediately can generalize and envision the object in different attributes , e.g. , in different 3D pose ( Logothetis et al. , 1995 ) . Primates can readily do so , as their previous kno... | The paper proposed a new training framework, namely GSL, for novel content synthesis. And GSL enables learning of disentangled representations of tangible attributes and achieve novel image synthesis by recombining those swappable components under a zero-shot setting. The framework leverages the underlying semantic lin... | SP:5561773ab024b083be4e362db079e371abf79653 |
Asymmetric self-play for automatic goal discovery in robotic manipulation | 1 INTRODUCTION . We are motivated to train a single goal-conditioned policy ( Kaelbling , 1993 ) that can solve any robotic manipulation task that a human may request in a given environment . In this work , we make progress towards this goal by solving a robotic manipulation problem in a table-top setting where the rob... | This paper presents an approach to learn goal conditioned policies by relying on self-play which sets the goals and discovers a curriculum of tasks for learning. Alice and Bob are the agents. Alice's task is to set a goal by following a number of steps in the environment and she is rewarded when the goal is too challen... | SP:9f70871f0111b58783f731748d8750c635998f32 |
Transfer Learning of Graph Neural Networks with Ego-graph Information Maximization | 1 Introduction . Graph neural networks ( GNNs ) have been intensively studied recently [ 29 , 26 , 39 , 68 ] , due to their established performance towards various real-world tasks [ 15 , 69 , 53 ] , as well as close connections to spectral graph theory [ 12 , 9 , 16 ] . While most GNN architectures are not very compli... | The paper introduces a theoretical framework for analyzing GNN transferability. The main idea is to view a graph as subgraph samples with the information of both the connections and the features. Based on this view, the authors define EGI score of a graph as a learnable function that needs to be optimized by maximizing... | SP:038a1d3066f8273977337262e975d7a7aab5002f |
Information Lattice Learning | 1 INTRODUCTION . With rapid progress in AI , there is an increasing desire for general AI ( Goertzel & Pennachin , 2007 ; Chollet , 2019 ) and explainable AI ( Adadi & Berrada , 2018 ; Molnar , 2019 ) , which exhibit broad , human-like cognitive capacities . One common pursuit is to move away from β black boxes β desig... | The authors perform a descriptive analysis of data by attempting to identify elements in the partial ordering of all partitions on the data which admit a compact definition. Compact definitions are those that are formed by composition of a small number of predefined (prior) set of mathematical operations. Projection an... | SP:40cba7b6c04d7e44709baed351382c27fa89a129 |
Don't be picky, all students in the right family can learn from good teachers | 1 INTRODUCTION . Recently-developed deep learning models have achieved remarkable performance in a variety of tasks . However , breakthroughs leading to state-of-the-art ( SOTA ) results often rely on very large models : GPipe , Big Transfer and GPT-3 use 556 million , 928 million and 175 billion parameters , respectiv... | This paper proposes searching for an architecture generator that outputs good student architectures for a given teacher. The authors claim that by learning the parameters of the generator instead of relying directly on the search space, it is possible to explore the search space of architectures more effectively, incre... | SP:1ee00313e354c4594bbf6cf8bdbe33e3ec8df62f |
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MuP - Multi Perspective Scientific Document Summarization
Generating summaries of scientific documents is known to be a challenging task. Majority of existing work in summarization assumes only one single best gold summary for each given document. Having only one gold summary negatively impacts our ability to evaluate the quality of summarization systems as writing summaries is a subjective activity. At the same time, annotating multiple gold summaries for scientific documents can be extremely expensive as it requires domain experts to read and understand long scientific documents. This shared task will enable exploring methods for generating multi-perspective summaries. We introduce a novel summarization corpus, leveraging data from scientific peer reviews to capture diverse perspectives from the reader's point of view.
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