Search Results for author:
Found 125 papers, 68 papers with code
Aligning Generative Language Models with Human Values
Although current large-scale generative language models (LMs) can show impressive insights about factual knowledge, they do not exhibit similar success with respect to human values judgements (e. g., whether or not the generations of an LM are moral).
FormalMATH: Benchmarking Formal Mathematical Reasoning of Large Language Models
Formal mathematical reasoning remains a critical challenge for artificial intelligence, hindered by limitations of existing benchmarks in scope and scale.
IV-Bench: A Benchmark for Image-Grounded Video Perception and Reasoning in Multimodal LLMs
Existing evaluation frameworks for Multimodal Large Language Models (MLLMs) primarily focus on image reasoning or general video understanding tasks, largely overlooking the significant role of image context in video comprehension.
ReTool: Reinforcement Learning for Strategic Tool Use in LLMs
While reasoning models (e. g., DeepSeek R1) trained with reinforcement learning (RL), excel in textual reasoning, they struggle in scenarios requiring structured problem-solving, such as geometric reasoning, concise computation, or complex equation solving-areas where computational tools like code interpreters (CI) demonstrate distinct advantages.
Seed1.5-Thinking: Advancing Superb Reasoning Models with Reinforcement Learning
We introduce Seed1. 5-Thinking, capable of reasoning through thinking before responding, resulting in improved performance on a wide range of benchmarks.
Video SimpleQA: Towards Factuality Evaluation in Large Video Language Models
Recent advancements in Large Video Language Models (LVLMs) have highlighted their potential for multi-modal understanding, yet evaluating their factual grounding in video contexts remains a critical unsolved challenge.
A Comprehensive Survey on Long Context Language Modeling
With the growing number of long documents, dialogues, and other textual data, it is important to develop Long Context Language Models (LCLMs) that can process and analyze extensive inputs in an effective and efficient way.
High-entropy Advantage in Neural Networks' Generalizability
By employing molecular simulation algorithms, we compute entropy landscapes as functions of both training loss and test accuracy (or test loss), on networks with up to 1 million parameters, across four distinct machine learning tasks: arithmetic question, real-world tabular data, image recognition, and language modeling.
FlexWorld: Progressively Expanding 3D Scenes for Flexiable-View Synthesis
Generating flexible-view 3D scenes, including 360{\deg} rotation and zooming, from single images is challenging due to a lack of 3D data.
Vamba: Understanding Hour-Long Videos with Hybrid Mamba-Transformers
State-of-the-art transformer-based large multimodal models (LMMs) struggle to handle hour-long video inputs due to the quadratic complexity of the causal self-attention operations, leading to high computational costs during training and inference.
YuE: Scaling Open Foundation Models for Long-Form Music Generation
We tackle the task of long-form music generation--particularly the challenging \textbf{lyrics-to-song} problem--by introducing YuE, a family of open foundation models based on the LLaMA2 architecture.
Can Large Language Models Detect Errors in Long Chain-of-Thought Reasoning?
In this paper, to understand the qualities of these long CoTs and measure the critique abilities of existing LLMs on these long CoTs, we introduce the DeltaBench, including the generated long CoTs from different o1-like models (e. g., QwQ, DeepSeek-R1) for different reasoning tasks (e. g., Math, Code, General Reasoning), to measure the ability to detect errors in long CoT reasoning.
CodeCriticBench: A Holistic Code Critique Benchmark for Large Language Models
To address these limitations, we introduce a holistic code critique benchmark for LLMs called CodeCriticBench.
Audio-FLAN: A Preliminary Release
Audio-FLAN lays the foundation for unified audio-language models that can seamlessly handle both understanding (e. g., transcription, comprehension) and generation (e. g., speech, music, sound) tasks across a wide range of audio domains in a zero-shot manner.
SuperGPQA: Scaling LLM Evaluation across 285 Graduate Disciplines
To address this gap, we present SuperGPQA, a comprehensive benchmark that evaluates graduate-level knowledge and reasoning capabilities across 285 disciplines.
SimpleVQA: Multimodal Factuality Evaluation for Multimodal Large Language Models
The increasing application of multi-modal large language models (MLLMs) across various sectors have spotlighted the essence of their output reliability and accuracy, particularly their ability to produce content grounded in factual information (e. g. common and domain-specific knowledge).
CryptoX : Compositional Reasoning Evaluation of Large Language Models
In this paper, we introduce CryptoX, an evaluation framework that, for the first time, combines existing benchmarks and cryptographic, to quantify the compositional reasoning capacity of LLMs.
Generating Symbolic World Models via Test-time Scaling of Large Language Models
Solving complex planning problems requires Large Language Models (LLMs) to explicitly model the state transition to avoid rule violations, comply with constraints, and ensure optimality-a task hindered by the inherent ambiguity of natural language.
Aligning Instruction Tuning with Pre-training
Instruction tuning enhances large language models (LLMs) to follow human instructions across diverse tasks, relying on high-quality datasets to guide behavior.
KARPA: A Training-free Method of Adapting Knowledge Graph as References for Large Language Model's Reasoning Path Aggregation
Leveraging knowledge graphs (KGs) as external knowledge sources has emerged as a viable solution, but existing methods for LLM-based knowledge graph question answering (KGQA) are often limited by step-by-step decision-making on KGs, restricting the global planning and reasoning capabilities of LLMs, or they require fine-tuning or pre-training on specific KGs.
Toward Modality Gap: Vision Prototype Learning for Weakly-supervised Semantic Segmentation with CLIP
To mitigate the impact of the modality gap, we propose a Vision Prototype Learning (VPL) framework, by introducing more representative vision prototypes.
Weakly supervised Semantic Segmentation
Weakly-Supervised Semantic Segmentation
Path-of-Thoughts: Extracting and Following Paths for Robust Relational Reasoning with Large Language Models
Large language models (LLMs) possess vast semantic knowledge but often struggle with complex reasoning tasks, particularly in relational reasoning problems such as kinship or spatial reasoning.
Next Token Prediction Towards Multimodal Intelligence: A Comprehensive Survey
As Large Language Models (LLMs) have advanced to unify understanding and generation tasks within the textual modality, recent research has shown that tasks from different modalities can also be effectively encapsulated within the NTP framework, transforming the multimodal information into tokens and predict the next one given the context.
Yi-Lightning Technical Report
This technical report presents Yi-Lightning, our latest flagship large language model (LLM).
PhysGame: Uncovering Physical Commonsense Violations in Gameplay Videos
In this paper, we propose PhysGame as a pioneering benchmark to evaluate physical commonsense violations in gameplay videos.
OmniEdit: Building Image Editing Generalist Models Through Specialist Supervision
This is due to the application of simple filtering methods like CLIP-score.
OpenCoder: The Open Cookbook for Top-Tier Code Large Language Models
To address the gap, we introduce OpenCoder, a top-tier code LLM that not only achieves performance comparable to leading models but also serves as an "open cookbook" for the research community.
MdEval: Massively Multilingual Code Debugging
Code large language models (LLMs) have made significant progress in code debugging by directly generating the correct code based on the buggy code snippet.
M2rc-Eval: Massively Multilingual Repository-level Code Completion Evaluation
Besides, the existing benchmarks usually report overall average scores of different languages, where the fine-grained abilities in different completion scenarios are ignored.
AutoKaggle: A Multi-Agent Framework for Autonomous Data Science Competitions
Data science tasks involving tabular data present complex challenges that require sophisticated problem-solving approaches.
Sparse Decomposition of Graph Neural Networks
Graph Neural Networks (GNN) exhibit superior performance in graph representation learning, but their inference cost can be high, due to an aggregation operation that can require a memory fetch for a very large number of nodes.
Can MLLMs Understand the Deep Implication Behind Chinese Images?
To fill the gap, we introduce the **C**hinese **I**mage **I**mplication understanding **Bench**mark, **CII-Bench**, which aims to assess the higher-order perception and understanding capabilities of MLLMs for Chinese images.
CLaMP 2: Multimodal Music Information Retrieval Across 101 Languages Using Large Language Models
Challenges in managing linguistic diversity and integrating various musical modalities are faced by current music information retrieval systems.
A Comparative Study on Reasoning Patterns of OpenAI's o1 Model
In our work, to investigate the reasoning patterns of o1, we compare o1 with existing Test-time Compute methods (BoN, Step-wise BoN, Agent Workflow, and Self-Refine) by using OpenAI's GPT-4o as a backbone on general reasoning benchmarks in three domains (i. e., math, coding, commonsense reasoning).
MTU-Bench: A Multi-granularity Tool-Use Benchmark for Large Language Models
Besides, all evaluation metrics of our MTU-Bench are based on the prediction results and the ground truth without using any GPT or human evaluation metrics.
Omni-MATH: A Universal Olympiad Level Mathematic Benchmark For Large Language Models
However, existing benchmarks like GSM8K or MATH are now being solved with high accuracy (e. g., OpenAI o1 achieves 94. 8\% on MATH dataset), indicating their inadequacy for truly challenging these models.
ING-VP: MLLMs cannot Play Easy Vision-based Games Yet
To bridge this gap, we present ING-VP, the first INteractive Game-based Vision Planning benchmark, specifically designed to evaluate the spatial imagination and multi-step reasoning abilities of MLLMs.
General Preference Modeling with Preference Representations for Aligning Language Models
In this paper, we introduce preference representation learning, an approach that embeds responses into a latent space to capture intricate preference structures efficiently, achieving linear query complexity.
MIO: A Foundation Model on Multimodal Tokens
In this paper, we introduce MIO, a novel foundation model built on multimodal tokens, capable of understanding and generating speech, text, images, and videos in an end-to-end, autoregressive manner.
HelloBench: Evaluating Long Text Generation Capabilities of Large Language Models
Therefore, we introduce the Hierarchical Long Text Generation Benchmark (HelloBench), a comprehensive, in-the-wild, and open-ended benchmark to evaluate LLMs' performance in generating long text.
OmniBench: Towards The Future of Universal Omni-Language Models
Recent advancements in multimodal large language models (MLLMs) have focused on integrating multiple modalities, yet their ability to simultaneously process and reason across different inputs remains underexplored.
Enhancing Logical Reasoning in Large Language Models through Graph-based Synthetic Data
Despite recent advances in training and prompting strategies for Large Language Models (LLMs), these models continue to face challenges with complex logical reasoning tasks that involve long reasoning chains.
LIME: Less Is More for MLLM Evaluation
However, many of these benchmarks include overly simple or uninformative samples, complicating the effective distinction of different MLLMs' performance.
MMMU-Pro: A More Robust Multi-discipline Multimodal Understanding Benchmark
This paper introduces MMMU-Pro, a robust version of the Massive Multi-discipline Multimodal Understanding and Reasoning (MMMU) benchmark.
Towards a Unified View of Preference Learning for Large Language Models: A Survey
Finally, based on our unified perspective, we explore the challenges and future research directions for aligning large language models with human preferences.
FuzzCoder: Byte-level Fuzzing Test via Large Language Model
In this work, we propose to adopt fine-tuned large language models (FuzzCoder) to learn patterns in the input files from successful attacks to guide future fuzzing explorations.
Foundation Models for Music: A Survey
In recent years, foundation models (FMs) such as large language models (LLMs) and latent diffusion models (LDMs) have profoundly impacted diverse sectors, including music.
TableBench: A Comprehensive and Complex Benchmark for Table Question Answering
Recent advancements in Large Language Models (LLMs) have markedly enhanced the interpretation and processing of tabular data, introducing previously unimaginable capabilities.
I-SHEEP: Self-Alignment of LLM from Scratch through an Iterative Self-Enhancement Paradigm
Large Language Models (LLMs) have achieved significant advancements, however, the common learning paradigm treats LLMs as passive information repositories, neglecting their potential for active learning and alignment.
Overview of the NLPCC 2024 Shared Task on Chinese Metaphor Generation
The goal of this shared task is to generate Chinese metaphors using machine learning techniques and effectively identifying basic components of metaphorical sentences.
MMRA: A Benchmark for Evaluating Multi-Granularity and Multi-Image Relational Association Capabilities in Large Visual Language Models
Moreover, we explored the ability of LVLMs to perceive image sequences within the context of our multi-image association task.
DDK: Distilling Domain Knowledge for Efficient Large Language Models
Despite the advanced intelligence abilities of large language models (LLMs) in various applications, they still face significant computational and storage demands.
LongIns: A Challenging Long-context Instruction-based Exam for LLMs
To address these issues, we propose the LongIns benchmark dataset, a challenging long-context instruction-based exam for LLMs, which is built based on the existing instruction datasets.
VideoScore: Building Automatic Metrics to Simulate Fine-grained Human Feedback for Video Generation
The main barrier is the lack of large-scale human-annotated dataset.
GIEBench: Towards Holistic Evaluation of Group Identity-based Empathy for Large Language Models
As large language models (LLMs) continue to develop and gain widespread application, the ability of LLMs to exhibit empathy towards diverse group identities and understand their perspectives is increasingly recognized as critical.
PIN: A Knowledge-Intensive Dataset for Paired and Interleaved Multimodal Documents
Recent advancements in Large Multimodal Models (LMMs) have leveraged extensive multimodal datasets to enhance capabilities in complex knowledge-driven tasks.
MMTE: Corpus and Metrics for Evaluating Machine Translation Quality of Metaphorical Language
Machine Translation (MT) has developed rapidly since the release of Large Language Models and current MT evaluation is performed through comparison with reference human translations or by predicting quality scores from human-labeled data.
VCR: A Task for Pixel-Level Complex Reasoning in Vision Language Models via Restoring Occluded Text
This task stems from the observation that text embedded in images is intrinsically different from common visual elements and natural language due to the need to align the modalities of vision, text, and text embedded in images.
II-Bench: An Image Implication Understanding Benchmark for Multimodal Large Language Models
The rapid advancements in the development of multimodal large language models (MLLMs) have consistently led to new breakthroughs on various benchmarks.
D-CPT Law: Domain-specific Continual Pre-Training Scaling Law for Large Language Models
To address the limitations of existing methods, inspired by the Scaling Law for performance prediction, we propose to investigate the Scaling Law of the Domain-specific Continual Pre-Training (D-CPT Law) to decide the optimal mixture ratio with acceptable training costs for LLMs of different sizes.
MMLU-Pro: A More Robust and Challenging Multi-Task Language Understanding Benchmark
In the age of large-scale language models, benchmarks like the Massive Multitask Language Understanding (MMLU) have been pivotal in pushing the boundaries of what AI can achieve in language comprehension and reasoning across diverse domains.
MAP-Neo: Highly Capable and Transparent Bilingual Large Language Model Series
To improve the transparency of LLMs, the research community has formed to open-source truly open LLMs (e. g., Pythia, Amber, OLMo), where more details (e. g., pre-training corpus and training code) are being provided.
ComposerX: Multi-Agent Symbolic Music Composition with LLMs
Music composition represents the creative side of humanity, and itself is a complex task that requires abilities to understand and generate information with long dependency and harmony constraints.
MuPT: A Generative Symbolic Music Pretrained Transformer
In this paper, we explore the application of Large Language Models (LLMs) to the pre-training of music.
Chinese Tiny LLM: Pretraining a Chinese-Centric Large Language Model
In this study, we introduce CT-LLM, a 2B large language model (LLM) that illustrates a pivotal shift towards prioritizing the Chinese language in developing LLMs.
CodeEditorBench: Evaluating Code Editing Capability of Large Language Models
Large Language Models (LLMs) for code are rapidly evolving, with code editing emerging as a critical capability.
Long-context LLMs Struggle with Long In-context Learning
We introduce a benchmark (LongICLBench) for long in-context learning in extreme-label classification using six datasets with 28 to 174 classes and input lengths from 2K to 50K tokens.
The Fine Line: Navigating Large Language Model Pretraining with Down-streaming Capability Analysis
To address the aforementioned limitations, this paper undertakes a comprehensive comparison of model capabilities at various pretraining intermediate checkpoints.
COIG-CQIA: Quality is All You Need for Chinese Instruction Fine-tuning
Remarkable progress on English instruction tuning has facilitated the efficacy and reliability of large language models (LLMs).
Yi: Open Foundation Models by 01.AI
The Yi model family is based on 6B and 34B pretrained language models, then we extend them to chat models, 200K long context models, depth-upscaled models, and vision-language models.
Ranked #1 on
Chatbot
on AlpacaEval
(using extra training data)
TEGEE: Task dEfinition Guided Expert Ensembling for Generalizable and Few-shot Learning
Our framework employs a dual 3B model approach, with each model assigned a distinct role: one focuses on task definition extraction, while the other handles learning from demonstrations.
StructLM: Towards Building Generalist Models for Structured Knowledge Grounding
Utilizing this dataset, we train a series of models, referred to as StructLM, based on the Mistral and the CodeLlama model family, ranging from 7B to 34B parameters.
ChatMusician: Understanding and Generating Music Intrinsically with LLM
It is based on continual pre-training and finetuning LLaMA2 on a text-compatible music representation, ABC notation, and the music is treated as a second language.
OpenCodeInterpreter: Integrating Code Generation with Execution and Refinement
However, open-source models often lack the execution capabilities and iterative refinement of advanced systems like the GPT-4 Code Interpreter.
MORE-3S:Multimodal-based Offline Reinforcement Learning with Shared Semantic Spaces
Drawing upon the intuition that aligning different modalities to the same semantic embedding space would allow models to understand states and actions more easily, we propose a new perspective to the offline reinforcement learning (RL) challenge.
CIF-Bench: A Chinese Instruction-Following Benchmark for Evaluating the Generalizability of Large Language Models
The advancement of large language models (LLMs) has enhanced the ability to generalize across a wide range of unseen natural language processing (NLP) tasks through instruction-following.
CMDAG: A Chinese Metaphor Dataset with Annotated Grounds as CoT for Boosting Metaphor Generation
These models are able to generate creative and fluent metaphor sentences more frequently induced by selected samples from our dataset, demonstrating the value of our corpus for Chinese metaphor research.
AnyGPT: Unified Multimodal LLM with Discrete Sequence Modeling
We introduce AnyGPT, an any-to-any multimodal language model that utilizes discrete representations for the unified processing of various modalities, including speech, text, images, and music.
ConsistI2V: Enhancing Visual Consistency for Image-to-Video Generation
To verify the effectiveness of our method, we propose I2V-Bench, a comprehensive evaluation benchmark for I2V generation.
Read to Play (R2-Play): Decision Transformer with Multimodal Game Instruction
Drawing inspiration from the success of multimodal instruction tuning in visual tasks, we treat the visual-based RL task as a long-horizon vision task and construct a set of multimodal game instructions to incorporate instruction tuning into a decision transformer.
SciMMIR: Benchmarking Scientific Multi-modal Information Retrieval
We further annotate the image-text pairs with two-level subset-subcategory hierarchy annotations to facilitate a more comprehensive evaluation of the baselines.
CMMMU: A Chinese Massive Multi-discipline Multimodal Understanding Benchmark
We introduce CMMMU, a new Chinese Massive Multi-discipline Multimodal Understanding benchmark designed to evaluate LMMs on tasks demanding college-level subject knowledge and deliberate reasoning in a Chinese context.
E^2-LLM: Efficient and Extreme Length Extension of Large Language Models
Typically, training LLMs with long context sizes is computationally expensive, requiring extensive training hours and GPU resources.
Kun: Answer Polishment for Chinese Self-Alignment with Instruction Back-Translation
In this paper, we introduce Kun, a novel approach for creating high-quality instruction-tuning datasets for large language models (LLMs) without relying on manual annotations.
Improving Depth Completion via Depth Feature Upsampling
In this paper we visualize the internal feature maps to analyze how the network densifies the input sparse depth.
Align on the Fly: Adapting Chatbot Behavior to Established Norms
In this paper, we aim to align large language models with the ever-changing, complex, and diverse human values (e. g., social norms) across time and locations.
UniIR: Training and Benchmarking Universal Multimodal Information Retrievers
Existing information retrieval (IR) models often assume a homogeneous format, limiting their applicability to diverse user needs, such as searching for images with text descriptions, searching for a news article with a headline image, or finding a similar photo with a query image.
MMMU: A Massive Multi-discipline Multimodal Understanding and Reasoning Benchmark for Expert AGI
We introduce MMMU: a new benchmark designed to evaluate multimodal models on massive multi-discipline tasks demanding college-level subject knowledge and deliberate reasoning.
Massive Editing for Large Language Models via Meta Learning
While large language models (LLMs) have enabled learning knowledge from the pre-training corpora, the acquired knowledge may be fundamentally incorrect or outdated over time, which necessitates rectifying the knowledge of the language model (LM) after the training.
LRRU: Long-short Range Recurrent Updating Networks for Depth Completion
Existing deep learning-based depth completion methods generally employ massive stacked layers to predict the dense depth map from sparse input data.
TIGERScore: Towards Building Explainable Metric for All Text Generation Tasks
To quantitatively assess our metric, we evaluate its correlation with human ratings on 5 held-in datasets, 2 held-out datasets and show that TIGERScore can achieve the open-source SoTA correlation with human ratings across these datasets and almost approaches GPT-4 evaluator.
AutoAgents: A Framework for Automatic Agent Generation
Therefore, we introduce AutoAgents, an innovative framework that adaptively generates and coordinates multiple specialized agents to build an AI team according to different tasks.
MusiLingo: Bridging Music and Text with Pre-trained Language Models for Music Captioning and Query Response
Large Language Models (LLMs) have shown immense potential in multimodal applications, yet the convergence of textual and musical domains remains not well-explored.
MAmmoTH: Building Math Generalist Models through Hybrid Instruction Tuning
The MAmmoTH models are trained on MathInstruct, our meticulously curated instruction tuning dataset.
Fragment and Integrate Network (FIN): A Novel Spatial-Temporal Modeling Based on Long Sequential Behavior for Online Food Ordering Click-Through Rate Prediction
(ii) Integrate Network (IN) builds a new integrated sequence by utilizing spatial-temporal interaction on MSS and captures the comprehensive spatial-temporal representation by modeling the integrated sequence with a complicated attention.
SoK: Comparing Different Membership Inference Attacks with a Comprehensive Benchmark
We have identified three principles for the proposed "comparing different MI attacks" methodology, and we have designed and implemented the MIBench benchmark with 84 evaluation scenarios for each dataset.
On the Effectiveness of Speech Self-supervised Learning for Music
Our findings suggest that training with music data can generally improve performance on MIR tasks, even when models are trained using paradigms designed for speech.
LyricWhiz: Robust Multilingual Zero-shot Lyrics Transcription by Whispering to ChatGPT
We introduce LyricWhiz, a robust, multilingual, and zero-shot automatic lyrics transcription method achieving state-of-the-art performance on various lyrics transcription datasets, even in challenging genres such as rock and metal.
MARBLE: Music Audio Representation Benchmark for Universal Evaluation
This is evident in the limited work on deep music representations, the scarcity of large-scale datasets, and the absence of a universal and community-driven benchmark.
MERT: Acoustic Music Understanding Model with Large-Scale Self-supervised Training
Although SSL has been proven effective in speech and audio, its application to music audio has yet to be thoroughly explored.
TPDM: Selectively Removing Positional Information for Zero-shot Translation via Token-Level Position Disentangle Module
Due to Multilingual Neural Machine Translation's (MNMT) capability of zero-shot translation, many works have been carried out to fully exploit the potential of MNMT in zero-shot translation.
Training Socially Aligned Language Models on Simulated Social Interactions
Social alignment in AI systems aims to ensure that these models behave according to established societal values.
Interactive Natural Language Processing
Interactive Natural Language Processing (iNLP) has emerged as a novel paradigm within the field of NLP, aimed at addressing limitations in existing frameworks while aligning with the ultimate goals of artificial intelligence.
RSC-VAE: Recoding Semantic Consistency Based VAE for One-Class Novelty Detection
While in this paper, we further exploit the latent space of Variational Auto-encoder (VAE), a typical reconstruction based model, and we innovatively divide it into three regions: Normal/Anomalous/Unknown-semantic-region.
Chinese Open Instruction Generalist: A Preliminary Release
Instruction tuning is widely recognized as a key technique for building generalist language models, which has attracted the attention of researchers and the public with the release of InstructGPT~\citep{ouyang2022training} and ChatGPT\footnote{\url{https://chat. openai. com/}}.
State of the Art and Potentialities of Graph-level Learning
Traditional approaches to learning a set of graphs heavily rely on hand-crafted features, such as substructures.
Second Thoughts are Best: Learning to Re-Align With Human Values from Text Edits
We present Second Thought, a new learning paradigm that enables language models (LMs) to re-align with human values.
CORGI-PM: A Chinese Corpus For Gender Bias Probing and Mitigation
As natural language processing (NLP) for gender bias becomes a significant interdisciplinary topic, the prevalent data-driven techniques such as large-scale language models suffer from data inadequacy and biased corpus, especially for languages with insufficient resources such as Chinese.
MAP-Music2Vec: A Simple and Effective Baseline for Self-Supervised Music Audio Representation Learning
The deep learning community has witnessed an exponentially growing interest in self-supervised learning (SSL).
HERB: Measuring Hierarchical Regional Bias in Pre-trained Language Models
In addition to verifying the existence of regional bias in LMs, we find that the biases on regional groups can be strongly influenced by the geographical clustering of the groups.
1Cademy @ Causal News Corpus 2022: Leveraging Self-Training in Causality Classification of Socio-Political Event Data
This paper details our participation in the Challenges and Applications of Automated Extraction of Socio-political Events from Text (CASE) workshop @ EMNLP 2022, where we take part in Subtask 1 of Shared Task 3.
1Cademy @ Causal News Corpus 2022: Enhance Causal Span Detection via Beam-Search-based Position Selector
In this paper, we present our approach and empirical observations for Cause-Effect Signal Span Detection -- Subtask 2 of Shared task 3~\cite{tan-etal-2022-event} at CASE 2022.
Correlation between entropy and generalizability in a neural network
Although neural networks can solve very complex machine-learning problems, the theoretical reason for their generalizability is still not fully understood.
1Cademy at Semeval-2022 Task 1: Investigating the Effectiveness of Multilingual, Multitask, and Language-Agnostic Tricks for the Reverse Dictionary Task
This paper describes our system for the SemEval2022 task of matching dictionary glosses to word embeddings.
Graph-level Neural Networks: Current Progress and Future Directions
To frame this survey, we propose a systematic taxonomy covering GLNNs upon deep neural networks, graph neural networks, and graph pooling.
Denoised Internal Models: a Brain-Inspired Autoencoder against Adversarial Attacks
Despite its great success, deep learning severely suffers from robustness; that is, deep neural networks are very vulnerable to adversarial attacks, even the simplest ones.
Spectral Variability Augmented Sparse Unmixing of Hyperspectral Images
Spectral unmixing (SU) expresses the mixed pixels existed in hyperspectral images as the product of endmember and abundance, which has been widely used in hyperspectral imagery analysis.
StrobeNet: Category-Level Multiview Reconstruction of Articulated Objects
We present StrobeNet, a method for category-level 3D reconstruction of articulating objects from one or more unposed RGB images.
Tilting the playing field: Dynamical loss functions for machine learning
In underparameterized networks, such dynamical loss functions can lead to successful training for networks that fail to find a deep minima of the standard cross-entropy loss.
Network-Agnostic Knowledge Transfer from Latent Dataset for Medical Image Segmentation
Transfer learning often employs all or part of the weights of a pre-trained net-work to the problem at hand; this limits the flexibility of new neural architectures.
Diverse Melody Generation from Chinese Lyrics via Mutual Information Maximization
In this paper, we propose to adapt the method of mutual information maximization into the task of Chinese lyrics conditioned melody generation to improve the generation quality and diversity.
CORAL: COde RepresentAtion Learning with Weakly-Supervised Transformers for Analyzing Data Analysis
Large scale analysis of source code, and in particular scientific source code, holds the promise of better understanding the data science process, identifying analytical best practices, and providing insights to the builders of scientific toolkits.
Self-Supervised Joint Learning Framework of Depth Estimation via Implicit Cues
Compared with static views, abundant dynamic properties between video frames are beneficial to refined depth estimation, especially for dynamic objects.
