pwc-archive/papers-with-abstracts huggingface dataset:
Transition-based Abstract Meaning Representation Parsing with Contextual Embeddings
Authors: Yichao Liang
Date: 2022-06-13 00:00:00
Tasks: Abstract Meaning Representation, AMR Parsing, Semantic Parsing, Word Embeddings

The ability to understand and generate languages sets human cognition apart from other known life forms'. We study a way of combing two of the most successful routes to meaning of language--statistical language models and symbolic semantics formalisms--in the task of semantic parsing. Building on a transition-based, Abstract Meaning Representation (AMR) parser, AmrEager, we explore the utility of incorporating pretrained context-aware word embeddings--such as BERT and RoBERTa--in the problem of AMR parsing, contributing a new parser we dub as AmrBerger. Experiments find these rich lexical features alone are not particularly helpful in improving the parser's overall performance as measured by the SMATCH score when compared to the non-contextual counterpart, while additional concept information empowers the system to outperform the baselines. Through lesion study, we found the use of contextual embeddings helps to make the system more robust against the removal of explicit syntactical features. These findings expose the strength and weakness of the contextual embeddings and the language models in the current form, and motivate deeper understanding thereof.
Symbolic Regression for Space Applications: Differentiable Cartesian Genetic Programming Powered by Multi-objective Memetic Algorithms
Authors: Marcus Märtens, Dario Izzo
Date: 2022-06-13 00:00:00
Tasks: regression, Symbolic Regression

Interpretable regression models are important for many application domains, as they allow experts to understand relations between variables from sparse data. Symbolic regression addresses this issue by searching the space of all possible free form equations that can be constructed from elementary algebraic functions. While explicit mathematical functions can be rediscovered this way, the determination of unknown numerical constants during search has been an often neglected issue. We propose a new multi-objective memetic algorithm that exploits a differentiable Cartesian Genetic Programming encoding to learn constants during evolutionary loops. We show that this approach is competitive or outperforms machine learned black box regression models or hand-engineered fits for two applications from space: the Mars express thermal power estimation and the determination of the age of stars by gyrochronology.
Constraint Guided Gradient Descent: Guided Training with Inequality Constraints
Authors: Quinten Van Baelen, Peter Karsmakers
Date: 2022-06-13 00:00:00

Deep learning is typically performed by learning a neural network solely from data in the form of input-output pairs ignoring available domain knowledge. In this work, the Constraint Guided Gradient Descent (CGGD) framework is proposed that enables the injection of domain knowledge into the training procedure. The domain knowledge is assumed to be described as a conjunction of hard inequality constraints which appears to be a natural choice for several applications. Compared to other neuro-symbolic approaches, the proposed method converges to a model that satisfies any inequality constraint on the training data and does not require to first transform the constraints into some ad-hoc term that is added to the learning (optimisation) objective. Under certain conditions, it is shown that CGGD can converges to a model that satisfies the constraints on the training set, while prior work does not necessarily converge to such a model. It is empirically shown on two independent and small data sets that CGGD makes training less dependent on the initialisation of the network and improves the constraint satisfiability on all data.
Transductive CLIP with Class-Conditional Contrastive Learning
Authors: Junchu Huang, WeiJie Chen, Shicai Yang, Di Xie, ShiLiang Pu, Yueting Zhuang
Date: 2022-06-13 00:00:00
Tasks: Contrastive Learning, Pseudo Label, Zero-shot Generalization

Inspired by the remarkable zero-shot generalization capacity of vision-language pre-trained model, we seek to leverage the supervision from CLIP model to alleviate the burden of data labeling. However, such supervision inevitably contains the label noise, which significantly degrades the discriminative power of the classification model. In this work, we propose Transductive CLIP, a novel framework for learning a classification network with noisy labels from scratch. Firstly, a class-conditional contrastive learning mechanism is proposed to mitigate the reliance on pseudo labels and boost the tolerance to noisy labels. Secondly, ensemble labels is adopted as a pseudo label updating strategy to stabilize the training of deep neural networks with noisy labels. This framework can reduce the impact of noisy labels from CLIP model effectively by combining both techniques. Experiments on multiple benchmark datasets demonstrate the substantial improvements over other state-of-the-art methods.
Causal Representation Learning for Instantaneous and Temporal Effects in Interactive Systems
Authors: Phillip Lippe, Sara Magliacane, Sindy Löwe, Yuki M. Asano, Taco Cohen, Efstratios Gavves
Date: 2022-06-13 00:00:00
Tasks: Causal Discovery, Representation Learning, Temporal Sequences

Causal representation learning is the task of identifying the underlying causal variables and their relations from high-dimensional observations, such as images. Recent work has shown that one can reconstruct the causal variables from temporal sequences of observations under the assumption that there are no instantaneous causal relations between them. In practical applications, however, our measurement or frame rate might be slower than many of the causal effects. This effectively creates "instantaneous" effects and invalidates previous identifiability results. To address this issue, we propose iCITRIS, a causal representation learning method that allows for instantaneous effects in intervened temporal sequences when intervention targets can be observed, e.g., as actions of an agent. iCITRIS identifies the potentially multidimensional causal variables from temporal observations, while simultaneously using a differentiable causal discovery method to learn their causal graph. In experiments on three datasets of interactive systems, iCITRIS accurately identifies the causal variables and their causal graph.
Absolute Expressiveness of Subgraph-based Centrality Measures
Authors: Andreas Pieris, Jorge Salas
Date: 2022-06-13 00:00:00

In graph-based applications, a common task is to pinpoint the most important or ``central'' vertex in a (directed or undirected) graph, or rank the vertices of a graph according to their importance. To this end, a plethora of so-called centrality measures have been proposed in the literature. Such measures assess which vertices in a graph are the most important ones by analyzing the structure of the underlying graph. A family of centrality measures that are suited for graph databases has been recently proposed by relying on the following simple principle: the importance of a vertex in a graph is relative to the number of ``relevant'' connected subgraphs surrounding it; we refer to the members of this family as subgraph-based centrality measures. Although it has been shown that such measures enjoy several favourable properties, their absolute expressiveness remains largely unexplored. The goal of this work is to precisely characterize the absolute expressiveness of the family of subgraph-based centrality measures by considering both directed and undirected graphs. To this end, we characterize when an arbitrary centrality measure is a subgraph-based one, or a subgraph-based measure relative to the induced ranking. These characterizations provide us with technical tools that allow us to determine whether well-established centrality measures are subgraph-based. Such a classification, apart from being interesting in its own right, gives useful insights on the structural similarities and differences among existing centrality measures.
Brain tumour segmentation with incomplete imaging data
Authors: James K Ruffle, Samia Mohinta, Robert J Gray, Harpreet Hyare, Parashkev Nachev
Date: 2022-06-13 00:00:00
Tasks: Segmentation

The complex heterogeneity of brain tumours is increasingly recognized to demand data of magnitudes and richness only fully-inclusive, large-scale collections drawn from routine clinical care could plausibly offer. This is a task contemporary machine learning could facilitate, especially in neuroimaging, but its ability to deal with incomplete data common in real world clinical practice remains unknown. Here we apply state-of-the-art methods to large scale, multi-site MRI data to quantify the comparative fidelity of automated tumour segmentation models replicating the various levels of sequence availability observed in the clinical reality. We compare deep learning (nnU-Net-derived) segmentation models with all possible combinations of T1, contrast-enhanced T1, T2, and FLAIR sequences, trained and validated with five-fold cross-validation on the 2021 BraTS-RSNA glioma population of 1251 patients, with further testing on a real-world 50 patient sample diverse in not only MRI scanner and field strength, but a random selection of pre- and post-operative imaging also. Models trained on incomplete imaging data segmented lesions well, often equivalently to those trained on complete data, exhibiting Dice coefficients of 0.907 (single sequence) to 0.945 (full datasets) for whole tumours, and 0.701 (single sequence) to 0.891 (full datasets) for component tissue types. Incomplete data segmentation models could accurately detect enhancing tumour in the absence of contrast imaging, quantifying its volume with an R2 between 0.95-0.97, and were invariant to lesion morphometry. Deep learning segmentation models characterize tumours well when missing data and can even detect enhancing tissue without the use of contrast. This suggests translation to clinical practice, where incomplete data is common, may be easier than hitherto believed, and may be of value in reducing dependence on contrast use.
Satellite-based high-resolution maps of cocoa planted area for Côte d'Ivoire and Ghana
Authors: Nikolai Kalischek, Nico Lang, Cécile Renier, Rodrigo Caye Daudt, Thomas Addoah, William Thompson, Wilma J. Blaser-Hart, Rachael Garrett, Konrad Schindler, Jan D. Wegner
Date: 2022-06-13 00:00:00

C\^ote d'Ivoire and Ghana, the world's largest producers of cocoa, account for two thirds of the global cocoa production. In both countries, cocoa is the primary perennial crop, providing income to almost two million farmers. Yet precise maps of cocoa planted area are missing, hindering accurate quantification of expansion in protected areas, production and yields, and limiting information available for improved sustainability governance. Here, we combine cocoa plantation data with publicly available satellite imagery in a deep learning framework and create high-resolution maps of cocoa plantations for both countries, validated in situ. Our results suggest that cocoa cultivation is an underlying driver of over 37% and 13% of forest loss in protected areas in C\^ote d'Ivoire and Ghana, respectively, and that official reports substantially underestimate the planted area, up to 40% in Ghana. These maps serve as a crucial building block to advance understanding of conservation and economic development in cocoa producing regions.
AR-NeRF: Unsupervised Learning of Depth and Defocus Effects from Natural Images with Aperture Rendering Neural Radiance Fields
Authors: Takuhiro Kaneko
Date: 2022-06-13 00:00:00
Proceeding: CVPR 2022 1
Tasks: NeRF, Representation Learning

Fully unsupervised 3D representation learning has gained attention owing to its advantages in data collection. A successful approach involves a viewpoint-aware approach that learns an image distribution based on generative models (e.g., generative adversarial networks (GANs)) while generating various view images based on 3D-aware models (e.g., neural radiance fields (NeRFs)). However, they require images with various views for training, and consequently, their application to datasets with few or limited viewpoints remains a challenge. As a complementary approach, an aperture rendering GAN (AR-GAN) that employs a defocus cue was proposed. However, an AR-GAN is a CNN-based model and represents a defocus independently from a viewpoint change despite its high correlation, which is one of the reasons for its performance. As an alternative to an AR-GAN, we propose an aperture rendering NeRF (AR-NeRF), which can utilize viewpoint and defocus cues in a unified manner by representing both factors in a common ray-tracing framework. Moreover, to learn defocus-aware and defocus-independent representations in a disentangled manner, we propose aperture randomized training, for which we learn to generate images while randomizing the aperture size and latent codes independently. During our experiments, we applied AR-NeRF to various natural image datasets, including flower, bird, and face images, the results of which demonstrate the utility of AR-NeRF for unsupervised learning of the depth and defocus effects.
Towards Autonomous Grading In The Real World
Authors: Yakov Miron, Chana Ross, Yuval Goldfracht, Chen Tessler, Dotan Di Castro
Date: 2022-06-13 00:00:00

In this work, we aim to tackle the problem of autonomous grading, where a dozer is required to flatten an uneven area. In addition, we explore methods for bridging the gap between a simulated environment and real scenarios. We design both a realistic physical simulation and a scaled real prototype environment mimicking the real dozer dynamics and sensory information. We establish heuristics and learning strategies in order to solve the problem. Through extensive experimentation, we show that although heuristics are capable of tackling the problem in a clean and noise-free simulated environment, they fail catastrophically when facing real world scenarios. As the heuristics are capable of successfully solving the task in the simulated environment, we show they can be leveraged to guide a learning agent which can generalize and solve the task both in simulation and in a scaled prototype environment.
Rank Diminishing in Deep Neural Networks
Authors: Ruili Feng, Kecheng Zheng, Yukun Huang, Deli Zhao, Michael Jordan, Zheng-Jun Zha
Date: 2022-06-13 00:00:00

The rank of neural networks measures information flowing across layers. It is an instance of a key structural condition that applies across broad domains of machine learning. In particular, the assumption of low-rank feature representations leads to algorithmic developments in many architectures. For neural networks, however, the intrinsic mechanism that yields low-rank structures remains vague and unclear. To fill this gap, we perform a rigorous study on the behavior of network rank, focusing particularly on the notion of rank deficiency. We theoretically establish a universal monotonic decreasing property of network rank from the basic rules of differential and algebraic composition, and uncover rank deficiency of network blocks and deep function coupling. By virtue of our numerical tools, we provide the first empirical analysis of the per-layer behavior of network rank in practical settings, i.e., ResNets, deep MLPs, and Transformers on ImageNet. These empirical results are in direct accord with our theory. Furthermore, we reveal a novel phenomenon of independence deficit caused by the rank deficiency of deep networks, where classification confidence of a given category can be linearly decided by the confidence of a handful of other categories. The theoretical results of this work, together with the empirical findings, may advance understanding of the inherent principles of deep neural networks.
Mediators: Conversational Agents Explaining NLP Model Behavior
Authors: Nils Feldhus, Ajay Madhavan Ravichandran, Sebastian Möller
Date: 2022-06-13 00:00:00
Tasks: Explainable artificial intelligence, model, Position, Sentiment Analysis

The human-centric explainable artificial intelligence (HCXAI) community has raised the need for framing the explanation process as a conversation between human and machine. In this position paper, we establish desiderata for Mediators, text-based conversational agents which are capable of explaining the behavior of neural models interactively using natural language. From the perspective of natural language processing (NLP) research, we engineer a blueprint of such a Mediator for the task of sentiment analysis and assess how far along current research is on the path towards dialogue-based explanations.
Virtual embeddings and self-consistency for self-supervised learning
Authors: Tariq Bdair, Hossam Abdelhamid, Nassir Navab, Shadi Albarqouni
Date: 2022-06-13 00:00:00
Tasks: Data Augmentation, Representation Learning, Self-Supervised Learning

Self-supervised Learning (SSL) has recently gained much attention due to the high cost and data limitation in the training of supervised learning models. The current paradigm in the SSL is to utilize data augmentation at the input space to create different views of the same images and train a model to maximize the representations between similar images and minimize them for different ones. While this approach achieves state-of-the-art (SOTA) results in various downstream tasks, it still lakes the opportunity to investigate the latent space augmentation. This paper proposes TriMix, a novel concept for SSL that generates virtual embeddings through linear interpolation of the data, thus providing the model with novel representations. Our strategy focuses on training the model to extract the original embeddings from virtual ones, hence, better representation learning. Additionally, we propose a self-consistency term that improves the consistency between the virtual and actual embeddings. We validate TriMix on eight benchmark datasets consisting of natural and medical images with an improvement of 2.71% and 0.41% better than the second-best models for both data types. Further, our approach outperformed the current methods in semi-supervised learning, particularly in low data regimes. Besides, our pre-trained models showed better transfer to other datasets.
Relative Policy-Transition Optimization for Fast Policy Transfer
Authors: Jiawei Xu, Cheng Zhou, Yizheng Zhang, Baoxiang Wang, Lei Han
Date: 2022-06-13 00:00:00
Tasks: continuous-control, Continuous Control, LEMMA, MuJoCo, Reinforcement Learning (RL)

We consider the problem of policy transfer between two Markov Decision Processes (MDPs). We introduce a lemma based on existing theoretical results in reinforcement learning to measure the relativity gap between two arbitrary MDPs, that is the difference between any two cumulative expected returns defined on different policies and environment dynamics. Based on this lemma, we propose two new algorithms referred to as Relative Policy Optimization (RPO) and Relative Transition Optimization (RTO), which offer fast policy transfer and dynamics modelling, respectively. RPO transfers the policy evaluated in one environment to maximize the return in another, while RTO updates the parameterized dynamics model to reduce the gap between the dynamics of the two environments. Integrating the two algorithms results in the complete Relative Policy-Transition Optimization (RPTO) algorithm, in which the policy interacts with the two environments simultaneously, such that data collections from two environments, policy and transition updates are completed in one closed loop to form a principled learning framework for policy transfer. We demonstrate the effectiveness of RPTO on a set of MuJoCo continuous control tasks by creating policy transfer problems via variant dynamics.
Intrinsically motivated option learning: a comparative study of recent methods
Authors: Djordje Božić, Predrag Tadić, Mladen Nikolić
Date: 2022-06-13 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning (RL)

Options represent a framework for reasoning across multiple time scales in reinforcement learning (RL). With the recent active interest in the unsupervised learning paradigm in the RL research community, the option framework was adapted to utilize the concept of empowerment, which corresponds to the amount of influence the agent has on the environment and its ability to perceive this influence, and which can be optimized without any supervision provided by the environment's reward structure. Many recent papers modify this concept in various ways achieving commendable results. Through these various modifications, however, the initial context of empowerment is often lost. In this work we offer a comparative study of such papers through the lens of the original empowerment principle.
Biologically Inspired Neural Path Finding
Authors: Hang Li, Qadeer Khan, Volker Tresp, Daniel Cremers
Date: 2022-06-13 00:00:00

The human brain can be considered to be a graphical structure comprising of tens of billions of biological neurons connected by synapses. It has the remarkable ability to automatically re-route information flow through alternate paths in case some neurons are damaged. Moreover, the brain is capable of retaining information and applying it to similar but completely unseen scenarios. In this paper, we take inspiration from these attributes of the brain, to develop a computational framework to find the optimal low cost path between a source node and a destination node in a generalized graph. We show that our framework is capable of handling unseen graphs at test time. Moreover, it can find alternate optimal paths, when nodes are arbitrarily added or removed during inference, while maintaining a fixed prediction time. Code is available here: https://github.com/hangligit/pathfinding
SIXO: Smoothing Inference with Twisted Objectives
Authors: Dieterich Lawson, Allan Raventós, Andrew Warrington, Scott Linderman
Date: 2022-06-13 00:00:00
Tasks: Density Ratio Estimation, State Space Models

Sequential Monte Carlo (SMC) is an inference algorithm for state space models that approximates the posterior by sampling from a sequence of target distributions. The target distributions are often chosen to be the filtering distributions, but these ignore information from future observations, leading to practical and theoretical limitations in inference and model learning. We introduce SIXO, a method that instead learns targets that approximate the smoothing distributions, incorporating information from all observations. The key idea is to use density ratio estimation to fit functions that warp the filtering distributions into the smoothing distributions. We then use SMC with these learned targets to define a variational objective for model and proposal learning. SIXO yields provably tighter log marginal lower bounds and offers significantly more accurate posterior inferences and parameter estimates in a variety of domains.