GenAI4ELab/papercli-papers huggingface dataset:
Approximate Inference in Logical Credal Networks
Authors: Radu Marinescu; Haifeng Qian; Alexander Gray; Debarun Bhattacharjya; Francisco Barahona; Tian Gao; Ryan Riegel
Venue: IJCAI 2023 (main)

Abstract: The Logical Credal Network or LCN is a recent probabilistic logic designed for effective aggregation and reasoning over multiple sources of imprecise knowledge. An LCN specifies a set of probability distributions over all interpretations of a set of logical formulas for which marginal and conditional probability bounds on their truth values are known. Inference in LCNs involves the exact solution of a non-convex non-linear program defined over an exponentially large number of non-negative real valued variables and, therefore, is limited to relatively small problems. In this paper, we present ARIEL -- a novel iterative message-passing scheme for approximate inference in LCNs. Inspired by classical belief propagation for graphical models, our method propagates messages that involve solving considerably smaller local non-linear programs. Experiments on several classes of LCNs demonstrate clearly that ARIEL yields high quality solutions compared with exact inference and scales to much larger problems than previously considered.
Structural Hawkes Processes for Learning Causal Structure from Discrete-Time Event Sequences
Authors: Jie Qiao; Ruichu Cai; Siyu Wu; Yu Xiang; Keli Zhang; Zhifeng Hao
Venue: IJCAI 2023 (main)

Abstract: Learning causal structure among event types from discrete-time event sequences is a particularly important but challenging task. Existing methods, such as the multivariate Hawkes processes based methods, mostly boil down to learning the so-called Granger causality which assumes that the cause event happens strictly prior to its effect event. Such an assumption is often untenable beyond applications, especially when dealing with discrete-time event sequences in low-resolution; and typical discrete Hawkes processes mainly suffer from identifiability issues raised by the instantaneous effect, i.e., the causal relationship that occurred simultaneously due to the low-resolution data will not be captured by Granger causality. In this work, we propose Structure Hawkes Processes (SHPs) that leverage the instantaneous effect for learning the causal structure among events type in discrete-time event sequence. The proposed method is featured with the Expectation-Maximization of the likelihood function and a sparse optimization scheme. Theoretical results show that the instantaneous effect is a blessing rather than a curse, and the causal structure is identifiable under the existence of the instantaneous effect. Experiments on synthetic and real-world data verify the effectiveness of the proposed method.
Distributional Multi-Objective Decision Making
Authors: Willem Röpke; Conor F. Hayes; Patrick Mannion; Enda Howley; Ann Nowé; Diederik M. Roijers
Venue: IJCAI 2023 (main)

Abstract: For effective decision support in scenarios with conflicting objectives, sets of potentially optimal solutions can be presented to the decision maker. We explore both what policies these sets should contain and how such sets can be computed efficiently. With this in mind, we take a distributional approach and introduce a novel dominance criterion relating return distributions of policies directly. Based on this criterion, we present the distributional undominated set and show that it contains optimal policies otherwise ignored by the Pareto front. In addition, we propose the convex distributional undominated set and prove that it comprises all policies that maximise expected utility for multivariate risk-averse decision makers. We propose a novel algorithm to learn the distributional undominated set and further contribute pruning operators to reduce the set to the convex distributional undominated set. Through experiments, we demonstrate the feasibility and effectiveness of these methods, making this a valuable new approach for decision support in real-world problems.
Finding an ϵ-Close Minimal Variation of Parameters in Bayesian Networks
Authors: Bahare Salmani; Joost-Pieter Katoen
Venue: IJCAI 2023 (main)

Abstract: This paper addresses the ε-close parameter tuning problem for Bayesian networks (BNs): find a minimal ε-close amendment of probability entries in a given set of (rows in) conditional probability tables that make a given quantitative constraint on the BN valid. Based on the state-of-the-art “region verification” techniques for parametric Markov chains, we propose an algorithm whose capabilities go beyond any existing techniques. Our experiments show that ε-close tuning of large BN benchmarks with up to eight parameters is feasible. In particular, by allowing (i) varied parameters in multiple CPTs and (ii) inter-CPT parameter dependencies, we treat subclasses of parametric BNs that have received scant attention so far.
The Hardness of Reasoning about Probabilities and Causality
Authors: Benito van der Zander; Markus Bläser; Maciej Liśkiewicz
Venue: IJCAI 2023 (main)

Abstract: We study formal languages which are capable of fully expressing quantitative probabilistic reasoning and do-calculus reasoning for causal effects, from a computational complexity perspective. We focus on satisfiability problems whose instance formulas allow expressing many tasks in probabilistic and causal inference. The main contribution of this work is establishing the exact computational complexity of these satisfiability problems. We introduce a new natural complexity class, named succ∃R, which can be viewed as a succinct variant of the well-studied class ∃R, and show that these problems are complete for succ∃R. Our results imply even stronger limitations on the use of algorithmic methods for reasoning about probabilities and causality than previous state-of-the-art results that rely only on the NP- or ∃R-completeness of the satisfiability problems for some restricted languages.
Safe Reinforcement Learning via Probabilistic Logic Shields
Authors: Wen-Chi Yang; Giuseppe Marra; Gavin Rens; Luc De Raedt
Venue: IJCAI 2023 (main)

Abstract: Safe Reinforcement learning (Safe RL) aims at learning optimal policies while staying safe. A popular solution to Safe RL is shielding, which uses a logical safety specification to prevent an RL agent from taking unsafe actions. However, traditional shielding techniques are difficult to integrate with continuous, end-to-end deep RL methods. To this end, we introduce Probabilistic Logic Policy Gradient (PLPG). PLPG is a model-based Safe RL technique that uses probabilistic logic programming to model logical safety constraints as differentiable functions. Therefore, PLPG can be seamlessly applied to any policy gradient algorithm while still providing the same convergence guarantees. In our experiments, we show that PLPG learns safer and more rewarding policies compared to other state-of-the-art shielding techniques.
Quantifying Consistency and Information Loss for Causal Abstraction Learning
Authors: Fabio Massimo Zennaro; Paolo Turrini; Theodoros Damoulas
Venue: IJCAI 2023 (main)

Abstract: Structural causal models provide a formalism to express causal relations between variables of interest. Models and variables can represent a system at different levels of abstraction, whereby relations may be coarsened and refined according to the need of a modeller. However, switching between different levels of abstraction requires evaluating a trade-off between the consistency and the information loss among different models. In this paper we introduce a family of interventional measures that an agent may use to evaluate such a trade-off. We consider four measures suited for different tasks, analyze their properties, and propose algorithms to evaluate and learn causal abstractions. Finally, we illustrate the flexibility of our setup by empirically showing how different measures and algorithmic choices may lead to different abstractions.
Max Markov Chain
Authors: Yu Zhang; Mitchell Bucklew
Venue: IJCAI 2023 (main)

Abstract: In this paper, we introduce Max Markov Chain (MMC), a novel model for sequential data with sparse correlations among the state variables. It may also be viewed as a special class of approximate models for High-order Markov Chains (HMCs). MMC is desirable for domains where the sparse correlations are long-term and vary in their temporal stretches. Although generally intractable, parameter optimization for MMC can be solved analytically. However, based on this result, we derive an approximate solution that is highly efficient empirically. When compared with HMC and approximate HMC models, MMC combines better sample efficiency, model parsimony, and an outstanding computational advantage. Such a quality allows MMC to scale to large domains where the competing models would struggle to perform. We compare MMC with several baselines with synthetic and real-world datasets to demonstrate MMC as a valuable alternative for stochastic modeling.
Evaluating Human-AI Interaction via Usability, User Experience and Acceptance Measures for MMM-C: A Creative AI System for Music Composition
Authors: Renaud Bougueng Tchemeube; Jeffrey Ens; Cale Plut; Philippe Pasquier; Maryam Safi; Yvan Grabit; Jean-Baptiste Rolland
Venue: IJCAI 2023 (main)

Abstract: With the rise of artificial intelligence (AI), there has been increasing interest in human-AI co-creation in a variety of artistic domains including music as AI-driven systems are frequently able to generate human-competitive artifacts. Now, the implications of such systems for the musical practice are being investigated. This paper reports on a thorough evaluation of the user adoption of the Multi-Track Music Machine (MMM) as a minimal co-creative AI tool for music composers. To do this, we integrate MMM into Cubase, a popular Digital Audio Workstation (DAW), by producing a "1-parameter" plugin interface named MMM-Cubase, which enables human-AI co-composition. We conduct a 3-part mixed method study measuring usability, user experience and technology acceptance of the system across two groups of expert-level composers: hobbyists and professionals. Results show positive usability and acceptance scores. Users report experiences of novelty, surprise and ease of use from using the system, and limitations on controllability and predictability of the interface when generating music. Findings indicate no significant difference between the two user groups.
The ACCompanion: Combining Reactivity, Robustness, and Musical Expressivity in an Automatic Piano Accompanist
Authors: Carlos Cancino-Chacón; Silvan Peter; Patricia Hu; Emmanouil Karystinaios; Florian Henkel; Francesco Foscarin; Gerhard Widmer
Venue: IJCAI 2023 (main)

Abstract: This paper introduces the ACCompanion, an expressive accompaniment system. Similarly to a musician who accompanies a soloist playing a given musical piece, our system can produce a human-like rendition of the accompaniment part that follows the soloist's choices in terms of tempo, dynamics, and articulation. The ACCompanion works in the symbolic domain, i.e., it needs a musical instrument capable of producing and playing MIDI data, with explicitly encoded onset, offset, and pitch for each played note. We describe the components that go into such a system, from real-time score following and prediction to expressive performance generation and online adaptation to the expressive choices of the human player. Based on our experience with repeated live demonstrations in front of various audiences, we offer an analysis of the challenges of combining these components into a system that is highly reactive and precise, while still a reliable musical partner, robust to possible performance errors and responsive to expressive variations.
TeSTNeRF: Text-Driven 3D Style Transfer via Cross-Modal Learning
Authors: Jiafu Chen; Boyan Ji; Zhanjie Zhang; Tianyi Chu; Zhiwen Zuo; Lei Zhao; Wei Xing; Dongming Lu
Venue: IJCAI 2023 (main)

Abstract: Text-driven 3D style transfer aims at stylizing a scene according to the text and generating arbitrary novel views with consistency. Simply combining image/video style transfer methods and novel view synthesis methods results in flickering when changing viewpoints, while existing 3D style transfer methods learn styles from images instead of texts. To address this problem, we for the first time design an efficient text-driven model for 3D style transfer, named TeSTNeRF, to stylize the scene using texts via cross-modal learning: we leverage an advanced text encoder to embed the texts in order to control 3D style transfer and align the input text and output stylized images in latent space. Furthermore, to obtain better visual results, we introduce style supervision, learning feature statistics from style images and utilizing 2D stylization results to rectify abrupt color spill. Extensive experiments demonstrate that TeSTNeRF significantly outperforms existing methods and provides a new way to guide 3D style transfer.
Graph-based Polyphonic Multitrack Music Generation
Authors: Emanuele Cosenza; Andrea Valenti; Davide Bacciu
Venue: IJCAI 2023 (main)

Abstract: Graphs can be leveraged to model polyphonic multitrack symbolic music, where notes, chords and entire sections may be linked at different levels of the musical hierarchy by tonal and rhythmic relationships. Nonetheless, there is a lack of works that consider graph representations in the context of deep learning systems for music generation. This paper bridges this gap by introducing a novel graph representation for music and a deep Variational Autoencoder that generates the structure and the content of musical graphs separately, one after the other, with a hierarchical architecture that matches the structural priors of music. By separating the structure and content of musical graphs, it is possible to condition generation by specifying which instruments are played at certain times. This opens the door to a new form of human-computer interaction in the context of music co-creation. After training the model on existing MIDI datasets, the experiments show that the model is able to generate appealing short and long musical sequences and to realistically interpolate between them, producing music that is tonally and rhythmically consistent. Finally, the visualization of the embeddings shows that the model is able to organize its latent space in accordance with known musical concepts.
Towards Symbiotic Creativity: A Methodological Approach to Compare Human and AI Robotic Dance Creations
Authors: Allegra De Filippo; Luca Giuliani; Eleonora Mancini; Andrea Borghesi; Paola Mello; Michela Milano
Venue: IJCAI 2023 (main)

Abstract: Artificial Intelligence (AI) has gradually attracted attention in the field of artistic creation, resulting in a debate on the evaluation of AI artistic outputs. However, there is a lack of common criteria for objective artistic evaluation both of human and AI creations. This is a frequent issue in the field of dance, where different performance metrics focus either on evaluating human or computational skills separately. This work proposes a methodological approach for the artistic evaluation of both AI and human artistic creations in the field of robotic dance. First, we define a series of common initial constraints to create robotic dance choreographies in a balanced initial setting, in collaboration with a group of human dancers and choreographer. Then, we compare both creation processes through a human audience evaluation. Finally, we investigate which choreography aspects (e.g., the music genre) have the largest impact on the evaluation, and we provide useful guidelines and future research directions for the analysis of interconnections between AI and human dance creation.
Automating Rigid Origami Design
Authors: Jeremia Geiger; Karolis Martinkus; Oliver Richter; Roger Wattenhofer
Venue: IJCAI 2023 (main)

Abstract: Rigid origami has shown potential in large diversity of practical applications. However, current rigid origami crease pattern design mostly relies on known tessellations. This strongly limits the diversity and novelty of patterns that can be created. In this work, we build upon the recently developed principle of three units method to formulate rigid origami design as a discrete optimization problem, the rigid origami game. Our implementation allows for a simple definition of diverse objectives and thereby expands the potential of rigid origami further to optimized, application-specific crease patterns. We showcase the flexibility of our formulation through use of a diverse set of search methods in several illustrative case studies. We are not only able to construct various patterns that approximate given target shapes, but to also specify abstract, function-based rewards which result in novel, foldable and functional designs for everyday objects.
Collaborative Neural Rendering Using Anime Character Sheets
Authors: Zuzeng Lin; Ailin Huang; Zhewei Huang
Venue: IJCAI 2023 (main)

Abstract: Drawing images of characters with desired poses is an essential but laborious task in anime production. Assisting artists to create is a research hotspot in recent years. In this paper, we present the Collaborative Neural Rendering (CoNR) method, which creates new images for specified poses from a few reference images (AKA Character Sheets). In general, the diverse hairstyles and garments of anime characters defies the employment of universal body models like SMPL, which fits in most nude human shapes. To overcome this, CoNR uses a compact and easy-to-obtain landmark encoding to avoid creating a unified UV mapping in the pipeline. In addition, the performance of CoNR can be significantly improved when referring to multiple reference images, thanks to feature space cross-view warping in a carefully designed neural network. Moreover, we have collected a character sheet dataset containing over 700,000 hand-drawn and synthesized images of diverse poses to facilitate research in this area. The code and dataset is available at https://github.com/megvii-research/IJCAI2023-CoNR.
IberianVoxel: Automatic Completion of Iberian Ceramics for Cultural Heritage Studies
Authors: Pablo Navarro; Celia Cintas; Manuel Lucena; José Manuel Fuertes; Antonio Rueda; Rafael Segura; Carlos Ogayar-Anguita; Rolando González-José; Claudio Delrieux
Venue: IJCAI 2023 (main)

Abstract: Accurate completion of archaeological artifacts is a critical aspect in several archaeological studies, including documentation of variations in style, inference of chronological and ethnic groups, and trading routes trends, among many others. However, most available pottery is fragmented, leading to missing textural and morphological cues. Currently, the reassembly and completion of fragmented ceramics is a daunting and time-consuming task, done almost exclusively by hand, which requires the physical manipulation of the fragments. To overcome the challenges of manual reconstruction, reduce the materials' exposure and deterioration, and improve the quality of reconstructed samples, we present IberianVoxel, a novel 3D Autoencoder Generative Adversarial Network (3D AE-GAN) framework tested on an extensive database with complete and fragmented references. We generated a collection of 1001 3D voxelized samples and their fragmented references from Iberian wheel-made pottery profiles. The fragments generated are stratified into different size groups and across multiple pottery classes. Lastly, we provide quantitative and qualitative assessments to measure the quality of the reconstructed voxelized samples by our proposed method and archaeologists' evaluation.
Discrete Diffusion Probabilistic Models for Symbolic Music Generation
Authors: Matthias Plasser; Silvan Peter; Gerhard Widmer
Venue: IJCAI 2023 (main)

Abstract: Denoising Diffusion Probabilistic Models (DDPMs) have made great strides in generating high-quality samples in both discrete and continuous domains. However, Discrete DDPMs (D3PMs) have yet to be applied to the domain of Symbolic Music. This work presents the direct generation of Polyphonic Symbolic Music using D3PMs. Our model exhibits state-of-the-art sample quality, according to current quantitative evaluation metrics, and allows for flexible infilling at the note level. We further show, that our models are accessible to post-hoc classifier guidance, widening the scope of possible applications. However, we also cast a critical view on quantitative evaluation of music sample quality via statistical metrics, and present a simple algorithm that can confound our metrics with completely spurious, non-musical samples.
Learn and Sample Together: Collaborative Generation for Graphic Design Layout
Authors: Haohan Weng; Danqing Huang; Tong Zhang; Chin-Yew Lin
Venue: IJCAI 2023 (main)

Abstract: In the process of graphic layout generation, user specifications including element attributes and their relationships are commonly used to constrain the layouts (e.g.,"put the image above the button''). It is natural to encode spatial constraints between elements using a graph. This paper presents a two-stage generation framework: a spatial graph generator and a subsequent layout decoder which is conditioned on the previous output graph. Training the two highly dependent networks separately as in previous work, we observe that the graph generator generates out-of-distribution graphs with a high frequency, which are unseen to the layout decoder during training and thus leads to huge performance drop in inference. To coordinate the two networks more effectively, we propose a novel collaborative generation strategy to perform round-way knowledge transfer between the networks in both training and inference. Experiment results on three public datasets show that our model greatly benefits from the collaborative generation and has achieved the state-of-the-art performance. Furthermore, we conduct an in-depth analysis to better understand the effectiveness of graph condition modeling.
DiffuseStyleGesture: Stylized Audio-Driven Co-Speech Gesture Generation with Diffusion Models
Authors: Sicheng Yang; Zhiyong Wu; Minglei Li; Zhensong Zhang; Lei Hao; Weihong Bao; Ming Cheng; Long Xiao
Venue: IJCAI 2023 (main)

Abstract: The art of communication beyond speech there are gestures. The automatic co-speech gesture generation draws much attention in computer animation. It is a challenging task due to the diversity of gestures and the difficulty of matching the rhythm and semantics of the gesture to the corresponding speech. To address these problems, we present DiffuseStyleGesture, a diffusion model based speech-driven gesture generation approach. It generates high-quality, speech-matched, stylized, and diverse co-speech gestures based on given speeches of arbitrary length. Specifically, we introduce cross-local attention and self-attention to the gesture diffusion pipeline to generate better speech matched and realistic gestures. We then train our model with classifier-free guidance to control the gesture style by interpolation or extrapolation. Additionally, we improve the diversity of generated gestures with different initial gestures and noise. Extensive experiments show that our method outperforms recent approaches on speech-driven gesture generation. Our code, pre-trained models, and demos are available at https://github.com/YoungSeng/DiffuseStyleGesture.