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Found 131 papers, 57 papers with code
What Makes Good In-Context Examples for GPT-3?
In this work, we investigate whether there are more effective strategies for judiciously selecting in-context examples (relative to random sampling) that better leverage GPT-3’s in-context learning capabilities. Inspired by the recent success of leveraging a retrieval module to augment neural networks, we propose to retrieve examples that are semantically-similar to a test query sample to formulate its corresponding prompt.
SAGE: Steering and Refining Dialog Generation with State-Action Augmentation
Recent advances in large language models have demonstrated impressive capabilities in task-oriented applications, yet building emotionally intelligent chatbots that can engage in natural, strategic conversations remains a challenge.
Reversal Blessing: Thinking Backward May Outpace Thinking Forward in Multi-choice Questions
Language models usually use left-to-right (L2R) autoregressive factorization.
Eliciting In-context Retrieval and Reasoning for Long-context Large Language Models
Recent advancements in long-context language models (LCLMs) promise to transform Retrieval-Augmented Generation (RAG) by simplifying pipelines.
Training Software Engineering Agents and Verifiers with SWE-Gym
When combined with our fine-tuned SWE agents, we achieve 32. 0% and 26. 0% on SWE-Bench Verified and Lite, respectively, reflecting a new state-of-the-art for open-weight SWE agents.
Learnable Prompting SAM-induced Knowledge Distillation for Semi-supervised Medical Image Segmentation
In this paper, we propose a learnable prompting SAM-induced Knowledge distillation framework (KnowSAM) for semi-supervised medical image segmentation.
On the Way to LLM Personalization: Learning to Remember User Conversations
Large Language Models (LLMs) have quickly become an invaluable assistant for a variety of tasks.
TypeScore: A Text Fidelity Metric for Text-to-Image Generative Models
In this work, we focus on the text rendering aspect of these models, which provides a lens for evaluating a generative model's fine-grained instruction-following capabilities.
Scaling Diffusion Language Models via Adaptation from Autoregressive Models
Diffusion Language Models (DLMs) have emerged as a promising new paradigm for text generative modeling, potentially addressing limitations of autoregressive (AR) models.
DART: Denoising Autoregressive Transformer for Scalable Text-to-Image Generation
Diffusion models have become the dominant approach for visual generation.
TSBP: Improving Object Detection in Histology Images via Test-time Self-guided Bounding-box Propagation
Because of this, using a preset global threshold (e. g., 0. 5) applied to all the bounding box candidates may lead to suboptimal solutions.
Towards Ground-truth-free Evaluation of Any Segmentation in Medical Images
We name this model EvanySeg (Evaluation of Any Segmentation in Medical Images).
On the Limited Generalization Capability of the Implicit Reward Model Induced by Direct Preference Optimization
These findings highlight that DPORM has limited generalization ability and substantiates the integration of an explicit reward model in iterative DPO approaches.
Adaptify: A Refined Adaptation Scheme for Frame Classification in Atrophic Gastritis Videos
Atrophic gastritis is a significant risk factor for developing gastric cancer.
ToolSandbox: A Stateful, Conversational, Interactive Evaluation Benchmark for LLM Tool Use Capabilities
Recent large language models (LLMs) advancements sparked a growing research interest in tool assisted LLMs solving real-world challenges, which calls for comprehensive evaluation of tool-use capabilities.
OpenHands: An Open Platform for AI Software Developers as Generalist Agents
OpenDevin), a platform for the development of powerful and flexible AI agents that interact with the world in similar ways to those of a human developer: by writing code, interacting with a command line, and browsing the web.
MMAU: A Holistic Benchmark of Agent Capabilities Across Diverse Domains
Ultimately, MMAU not only sheds light on the capabilities and limitations of LLM agents but also enhances the interpretability of their performance.
EndoFinder: Online Image Retrieval for Explainable Colorectal Polyp Diagnosis
To overcome this limitation, we introduce EndoFinder, a content-based image retrieval framework to find the 'digital twin' polyp in the reference database given a newly detected polyp.
Test-Time Generative Augmentation for Medical Image Segmentation
In this paper, we propose a novel approach to enhance medical image segmentation during test time.
An Empirical Study on the Fairness of Foundation Models for Multi-Organ Image Segmentation
We test state-of-the-art foundation models for medical image segmentation, including the original SAM, medical SAM and SAT models, to evaluate segmentation efficacy across different demographic groups and identify disparities.
Boosting Medical Image Classification with Segmentation Foundation Model
The Segment Anything Model (SAM) exhibits impressive capabilities in zero-shot segmentation for natural images.
Improving Segment Anything on the Fly: Auxiliary Online Learning and Adaptive Fusion for Medical Image Segmentation
We employ rectified annotations to perform online learning, with the aim of improving the segmentation quality of SA on medical images.
Improving GFlowNets for Text-to-Image Diffusion Alignment
Diffusion models have become the de-facto approach for generating visual data, which are trained to match the distribution of the training dataset.
Kaleido Diffusion: Improving Conditional Diffusion Models with Autoregressive Latent Modeling
Furthermore, we show that Kaleido adheres closely to the guidance provided by the generated latent variables, demonstrating its capability to effectively control and direct the image generation process.
Navigating User Experience of ChatGPT-based Conversational Recommender Systems: The Effects of Prompt Guidance and Recommendation Domain
Therefore, we have developed a ChatGPT-based CRS to investigate the impact of these two factors, prompt guidance (PG) and recommendation domain (RD), on the overall user experience of the system.
Many-to-many Image Generation with Auto-regressive Diffusion Models
This paper introduces a domain-general framework for many-to-many image generation, capable of producing interrelated image series from a given set of images, offering a scalable solution that obviates the need for task-specific solutions across different multi-image scenarios.
Automatic and Universal Prompt Injection Attacks against Large Language Models
Large Language Models (LLMs) excel in processing and generating human language, powered by their ability to interpret and follow instructions.
How Far Are We from Intelligent Visual Deductive Reasoning?
Vision-Language Models (VLMs) have recently demonstrated incredible strides on diverse vision language tasks.
Exploring the Design of Generative AI in Supporting Music-based Reminiscence for Older Adults
Music-based reminiscence has the potential to positively impact the psychological well-being of older adults.
Divide-or-Conquer? Which Part Should You Distill Your LLM?
Recent methods have demonstrated that Large Language Models (LLMs) can solve reasoning tasks better when they are encouraged to solve subtasks of the main task first.
Executable Code Actions Elicit Better LLM Agents
LLM agents are typically prompted to produce actions by generating JSON or text in a pre-defined format, which is usually limited by constrained action space (e. g., the scope of pre-defined tools) and restricted flexibility (e. g., inability to compose multiple tools).
Rephrasing the Web: A Recipe for Compute and Data-Efficient Language Modeling
Large language models are trained on massive scrapes of the web, which are often unstructured, noisy, and poorly phrased.
Dual-scale Enhanced and Cross-generative Consistency Learning for Semi-supervised Medical Image Segmentation
To address scale variation, we present a scale-enhanced consistency constraint, which ensures consistency in the segmentation maps generated from the same input image at different scales.
SQA-SAM: Segmentation Quality Assessment for Medical Images Utilizing the Segment Anything Model
Segmentation quality assessment (SQA) plays a critical role in the deployment of a medical image based AI system.
KGLens: Towards Efficient and Effective Knowledge Probing of Large Language Models with Knowledge Graphs
Large Language Models (LLMs) might hallucinate facts, while curated Knowledge Graph (KGs) are typically factually reliable especially with domain-specific knowledge.
Segment Anything Model-guided Collaborative Learning Network for Scribble-supervised Polyp Segmentation
Specifically, we first propose a Cross-level Enhancement and Aggregation Network (CEA-Net) for weakly-supervised polyp segmentation.
A Survey on Deep Learning for Polyp Segmentation: Techniques, Challenges and Future Trends
This paper provides a comprehensive review of polyp segmentation algorithms.
Matryoshka Diffusion Models
Diffusion models are the de facto approach for generating high-quality images and videos, but learning high-dimensional models remains a formidable task due to computational and optimization challenges.
Knowledge Extraction and Distillation from Large-Scale Image-Text Colonoscopy Records Leveraging Large Language and Vision Models
EndoKED automates the transformation of raw colonoscopy records into image datasets with pixel-level annotation.
Probing the Multi-turn Planning Capabilities of LLMs via 20 Question Games
In this paper, we offer a surrogate problem which assesses an LLMs's capability to deduce an entity unknown to itself, but revealed to a judge, by asking the judge a series of queries.
Edge-aware Feature Aggregation Network for Polyp Segmentation
Besides, a Scale-aware Convolution Module (SCM) is proposed to learn scale-aware features by using dilated convolutions with different ratios, in order to effectively deal with scale variation.
RR-CP: Reliable-Region-Based Conformal Prediction for Trustworthy Medical Image Classification
Conformal prediction (CP) generates a set of predictions for a given test sample such that the prediction set almost always contains the true label (e. g., 99. 5\% of the time).
SamDSK: Combining Segment Anything Model with Domain-Specific Knowledge for Semi-Supervised Learning in Medical Image Segmentation
Our new method is iterative and consists of two main stages: (1) segmentation model training; (2) expanding the labeled set by using the trained segmentation model, an unlabeled set, SAM, and domain-specific knowledge.
BOOT: Data-free Distillation of Denoising Diffusion Models with Bootstrapping
Diffusion models have demonstrated excellent potential for generating diverse images.
PLANNER: Generating Diversified Paragraph via Latent Language Diffusion Model
Autoregressive models for text sometimes generate repetitive and low-quality output because errors accumulate during the steps of generation.
Input Augmentation with SAM: Boosting Medical Image Segmentation with Segmentation Foundation Model
Thus, how to utilize such a large foundation model for medical image segmentation is an emerging research target.
Can SAM Segment Polyps?
Recently, Meta AI Research releases a general Segment Anything Model (SAM), which has demonstrated promising performance in several segmentation tasks.
Stabilizing Transformer Training by Preventing Attention Entropy Collapse
We show that $\sigma$Reparam provides stability and robustness with respect to the choice of hyperparameters, going so far as enabling training (a) a Vision Transformer {to competitive performance} without warmup, weight decay, layer normalization or adaptive optimizers; (b) deep architectures in machine translation and (c) speech recognition to competitive performance without warmup and adaptive optimizers.
Interactive Text Generation
Unfortunately, this means most of the research on text, code, and image generation has focused on non-interactive settings, whereby the model is expected to get everything right without accounting for any input from a user who may be willing to help.
GPT4MIA: Utilizing Generative Pre-trained Transformer (GPT-3) as A Plug-and-Play Transductive Model for Medical Image Analysis
In this paper, we propose a novel approach (called GPT4MIA) that utilizes Generative Pre-trained Transformer (GPT) as a plug-and-play transductive inference tool for medical image analysis (MIA).
EndoBoost: a plug-and-play module for false positive suppression during computer-aided polyp detection in real-world colonoscopy (with dataset)
In addition, the released dataset can be used to perform 'stress' tests on established detection systems and encourages further research toward robust and reliable computer-aided endoscopic image analysis.
Bridging the Training-Inference Gap for Dense Phrase Retrieval
Building dense retrievers requires a series of standard procedures, including training and validating neural models and creating indexes for efficient search.
f-DM: A Multi-stage Diffusion Model via Progressive Signal Transformation
In this work, we propose f-DM, a generalized family of DMs which allows progressive signal transformation.
Data-Driven Deep Supervision for Skin Lesion Classification
Specifically, we analyze the convolutional network's behavior (field-of-view) to find the location of deep supervision for improved feature extraction.
Usable Region Estimate for Assessing Practical Usability of Medical Image Segmentation Models
We aim to quantitatively measure the practical usability of medical image segmentation models: to what extent, how often, and on which samples a model's predictions can be used/trusted.
Data-Driven Deep Supervision for Medical Image Segmentation
In this paper, we study medical image segmentation by focusing on robust data-specific feature extraction to achieve improved dense prediction.
H-EMD: A Hierarchical Earth Mover's Distance Method for Instance Segmentation
We observe that probability maps by DL semantic segmentation models can be used to generate many possible instance candidates, and accurate instance segmentation can be achieved by selecting from them a set of "optimized" candidates as output instances.
Linearizing Transformer with Key-Value Memory
We demonstrate that MemSizer provides an improved balance between efficiency and accuracy over the vanilla transformer and other efficient transformer variants in three typical sequence generation tasks, including machine translation, abstractive text summarization, and language modeling.
Report from the NSF Future Directions Workshop on Automatic Evaluation of Dialog: Research Directions and Challenges
This is a report on the NSF Future Directions Workshop on Automatic Evaluation of Dialog.
Real-Time, Accurate, and Consistent Video Semantic Segmentation via Unsupervised Adaptation and Cross-Unit Deployment on Mobile Device
Such a deployment scheme best utilizes the available processing power on the smartphone and enables real-time operation of our adaptive video segmentation algorithm.
Towards More Efficient Insertion Transformer with Fractional Positional Encoding
Nevertheless, due to the incompatibility between absolute positional encoding and insertion-based generation schemes, it needs to refresh the encoding of every token in the generated partial hypothesis at each step, which could be costly.
HS3: Learning with Proper Task Complexity in Hierarchically Supervised Semantic Segmentation
While deeply supervised networks are common in recent literature, they typically impose the same learning objective on all transitional layers despite their varying representation powers.
Ranked #4 on
Semantic Segmentation
on Cityscapes test
X-Distill: Improving Self-Supervised Monocular Depth via Cross-Task Distillation
In order to enable such knowledge distillation across two different visual tasks, we introduce a small, trainable network that translates the predicted depth map to a semantic segmentation map, which can then be supervised by the teacher network.
Perceptual Consistency in Video Segmentation
More specifically, by measuring the perceptual consistency between the predicted segmentation and the available ground truth on a nearby frame and combining it with the segmentation confidence, we can accurately assess the classification correctness on each pixel.
AuxAdapt: Stable and Efficient Test-Time Adaptation for Temporally Consistent Video Semantic Segmentation
Since inconsistency mainly arises from the model's uncertainty in its output, we propose an adaptation scheme where the model learns from its own segmentation decisions as it streams a video, which allows producing more confident and temporally consistent labeling for similarly-looking pixels across frames.
Automatic Document Sketching: Generating Drafts from Analogous Texts
The advent of large pre-trained language models has made it possible to make high-quality predictions on how to add or change a sentence in a document.
RetGen: A Joint framework for Retrieval and Grounded Text Generation Modeling
We propose a framework that alleviates this data constraint by jointly training a grounded generator and document retriever on the language model signal.
A Token-level Reference-free Hallucination Detection Benchmark for Free-form Text Generation
Large pretrained generative models like GPT-3 often suffer from hallucinating non-existent or incorrect content, which undermines their potential merits in real applications.
An Adversarially-Learned Turing Test for Dialog Generation Models
To alleviate this risk, we propose an adversarial training approach to learn a robust model, ATT (Adversarial Turing Test), that discriminates machine-generated responses from human-written replies.
InverseForm: A Loss Function for Structured Boundary-Aware Segmentation
We present a novel boundary-aware loss term for semantic segmentation using an inverse-transformation network, which efficiently learns the degree of parametric transformations between estimated and target boundaries.
Ranked #5 on
Semantic Segmentation
on Cityscapes test
Finetuning Pretrained Transformers into RNNs
Specifically, we propose a swap-then-finetune procedure: in an off-the-shelf pretrained transformer, we replace the softmax attention with its linear-complexity recurrent alternative and then finetune.
Ranked #2 on
Machine Translation
on WMT2017 Chinese-English
Data Augmentation for Abstractive Query-Focused Multi-Document Summarization
The progress in Query-focused Multi-Document Summarization (QMDS) has been limited by the lack of sufficient largescale high-quality training datasets.
What Makes Good In-Context Examples for GPT-$3$?
Inspired by the recent success of leveraging a retrieval module to augment large-scale neural network models, we propose to retrieve examples that are semantically-similar to a test sample to formulate its corresponding prompt.
SDA: Improving Text Generation with Self Data Augmentation
Data augmentation has been widely used to improve deep neural networks in many research fields, such as computer vision.
Towards Robust and Efficient Contrastive Textual Representation Learning
There has been growing interest in representation learning for text data, based on theoretical arguments and empirical evidence.
Narrative Incoherence Detection
We propose the task of narrative incoherence detection as a new arena for inter-sentential semantic understanding: Given a multi-sentence narrative, decide whether there exist any semantic discrepancies in the narrative flow.
Unlabeled Data Guided Semi-supervised Histopathology Image Segmentation
Second, we devise an effective data selection policy for judiciously sampling the generated images: (1) to make the generated training set better cover the dataset, the clusters that are underrepresented in the original training set are covered more; (2) to make the training process more effective, we identify and oversample the images of "hard cases" in the data for which annotated training data may be scarce.
Improving Text Generation with Student-Forcing Optimal Transport
An extension is further proposed to improve the OT learning, based on the structural and contextual information of the text sequences.
Contextualized Perturbation for Textual Adversarial Attack
Adversarial examples expose the vulnerabilities of natural language processing (NLP) models, and can be used to evaluate and improve their robustness.
Dialogue Response Ranking Training with Large-Scale Human Feedback Data
Particularly, our ranker outperforms the conventional dialog perplexity baseline with a large margin on predicting Reddit feedback.
Weakly supervised cross-domain alignment with optimal transport
Cross-domain alignment between image objects and text sequences is key to many visual-language tasks, and it poses a fundamental challenge to both computer vision and natural language processing.
Structured Convolutions for Efficient Neural Network Design
We show how this decomposition can be applied to 2D and 3D kernels as well as the fully-connected layers.
DIALOGPT : Large-Scale Generative Pre-training for Conversational Response Generation
We present a large, tunable neural conversational response generation model, DIALOGPT (dialogue generative pre-trained transformer).
Improving Disentangled Text Representation Learning with Information-Theoretic Guidance
Learning disentangled representations of natural language is essential for many NLP tasks, e. g., conditional text generation, style transfer, personalized dialogue systems, etc.
POINTER: Constrained Progressive Text Generation via Insertion-based Generative Pre-training
Large-scale pre-trained language models, such as BERT and GPT-2, have achieved excellent performance in language representation learning and free-form text generation.
A Controllable Model of Grounded Response Generation
Current end-to-end neural conversation models inherently lack the flexibility to impose semantic control in the response generation process, often resulting in uninteresting responses.
Contextual Text Style Transfer
To realize high-quality style transfer with natural context preservation, we propose a Context-Aware Style Transfer (CAST) model, which uses two separate encoders for each input sentence and its surrounding context.
Optimus: Organizing Sentences via Pre-trained Modeling of a Latent Space
We hope that our first pre-trained big VAE language model itself and results can help the NLP community renew the interests of deep generative models in the era of large-scale pre-training, and make these principled methods more practical.
Adaptive Correlated Monte Carlo for Contextual Categorical Sequence Generation
To stabilize this method, we adapt to contextual generation of categorical sequences a policy gradient estimator, which evaluates a set of correlated Monte Carlo (MC) rollouts for variance control.
INSET: Sentence Infilling with INter-SEntential Transformer
Missing sentence generation (or sentence infilling) fosters a wide range of applications in natural language generation, such as document auto-completion and meeting note expansion.
Contrastive Multi-document Question Generation
In the SL stage, a single-document question generator is trained.
DialoGPT: Large-Scale Generative Pre-training for Conversational Response Generation
We present a large, tunable neural conversational response generation model, DialoGPT (dialogue generative pre-trained transformer).
Generating a Common Question from Multiple Documents using Multi-source Encoder-Decoder Models
A preliminary step towards this goal is to generate a question that captures common concepts of multiple documents.
Contrastively Smoothed Class Alignment for Unsupervised Domain Adaptation
Recent unsupervised approaches to domain adaptation primarily focus on minimizing the gap between the source and the target domains through refining the feature generator, in order to learn a better alignment between the two domains.
Structuring Latent Spaces for Stylized Response Generation
This structure allows the system to generate stylized relevant responses by sampling in the neighborhood of the conversation model prediction, and continuously control the style level.
Unsupervised Dialogue Spectrum Generation for Log Dialogue Ranking
Although the data-driven approaches of some recent bot building platforms make it possible for a wide range of users to easily create dialogue systems, those platforms don{'}t offer tools for quickly identifying which log dialogues contain problems.
Second-order Non-local Attention Networks for Person Re-identification
Recent efforts have shown promising results for person re-identification by designing part-based architectures to allow a neural network to learn discriminative representations from semantically coherent parts.
Domain Adaptive Text Style Transfer
These data may demonstrate domain shift, which impedes the benefits of utilizing such data for training.
Microsoft Icecaps: An Open-Source Toolkit for Conversation Modeling
The Intelligent Conversation Engine: Code and Pre-trained Systems (Microsoft Icecaps) is an upcoming open-source natural language processing repository.
Decompose-and-Integrate Learning for Multi-class Segmentation in Medical Images
Ablation study confirms the effectiveness of our proposed learning scheme for medical images.
Improving Textual Network Embedding with Global Attention via Optimal Transport
Constituting highly informative network embeddings is an important tool for network analysis.
Consistent Dialogue Generation with Self-supervised Feature Learning
Generating responses that are consistent with the dialogue context is one of the central challenges in building engaging conversational agents.
Jointly Optimizing Diversity and Relevance in Neural Response Generation
In this paper, we propose a SpaceFusion model to jointly optimize diversity and relevance that essentially fuses the latent space of a sequence-to-sequence model and that of an autoencoder model by leveraging novel regularization terms.
Ranked #1 on
Dialogue Generation
on Reddit (multi-ref)
SPDA: Superpixel-based Data Augmentation for Biomedical Image Segmentation
To better mimic human visual perception, we think it is desirable for the deep learning model to be able to perceive not only raw images but also SP images.
Improving Sequence-to-Sequence Learning via Optimal Transport
Sequence-to-sequence models are commonly trained via maximum likelihood estimation (MLE).
Cascade Decoder: A Universal Decoding Method for Biomedical Image Segmentation
The cascade decoder structure aims to conduct more effective decoding of hierarchically encoded features and is more compatible with common encoders than the known decoders.
A New Ensemble Learning Framework for 3D Biomedical Image Segmentation
In this paper, we propose a new ensemble learning framework for 3D biomedical image segmentation that combines the merits of 2D and 3D models.
Ranked #1 on
Cardiovascular MR Segmentaiton
on HVSMR 2016
Towards Coherent and Cohesive Long-form Text Generation
Generating coherent and cohesive long-form texts is a challenging task.
A bird's eye view on coherence, and a worm's eye view on cohesion
Generating coherent and cohesive long-form texts is a challenging problem in natural language generation.
Adversarial Text Generation via Feature-Mover's Distance
However, the discrete nature of text hinders the application of GAN to text-generation tasks.
Generating Informative and Diverse Conversational Responses via Adversarial Information Maximization
Responses generated by neural conversational models tend to lack informativeness and diversity.
JointGAN: Multi-Domain Joint Distribution Learning with Generative Adversarial Nets
Distinct from most existing approaches, that only learn conditional distributions, the proposed model aims to learn a joint distribution of multiple random variables (domains).
BoxNet: Deep Learning Based Biomedical Image Segmentation Using Boxes Only Annotation
In recent years, deep learning (DL) methods have become powerful tools for biomedical image segmentation.
Baseline Needs More Love: On Simple Word-Embedding-Based Models and Associated Pooling Mechanisms
Many deep learning architectures have been proposed to model the compositionality in text sequences, requiring a substantial number of parameters and expensive computations.
Ranked #1 on
Named Entity Recognition (NER)
on CoNLL 2000
Joint Embedding of Words and Labels for Text Classification
Word embeddings are effective intermediate representations for capturing semantic regularities between words, when learning the representations of text sequences.
Ranked #11 on
Text Classification
on DBpedia
A New Registration Approach for Dynamic Analysis of Calcium Signals in Organs
A key to analyzing spatial-temporal patterns of $Ca^{2+}$ signal waves is to accurately align the pouches across image sequences.
On the Use of Word Embeddings Alone to Represent Natural Language Sequences
In this paper, we conduct an extensive comparative study between Simple Word Embeddings-based Models (SWEMs), with no compositional parameters, relative to employing word embeddings within RNN/CNN-based models.
Zero-Shot Learning via Class-Conditioned Deep Generative Models
We present a deep generative model for learning to predict classes not seen at training time.
Triangle Generative Adversarial Networks
The generators are designed to learn the two-way conditional distributions between the two domains, while the discriminators implicitly define a ternary discriminative function, which is trained to distinguish real data pairs and two kinds of fake data pairs.
A Convergence Analysis for A Class of Practical Variance-Reduction Stochastic Gradient MCMC
However, there has been little theoretical analysis of the impact of minibatch size to the algorithm's convergence rate.
Deconvolutional Paragraph Representation Learning
Learning latent representations from long text sequences is an important first step in many natural language processing applications.
Suggestive Annotation: A Deep Active Learning Framework for Biomedical Image Segmentation
Image segmentation is a fundamental problem in biomedical image analysis.
Adversarial Feature Matching for Text Generation
We propose a framework for generating realistic text via adversarial training.
Stochastic Gradient Monomial Gamma Sampler
A framework is proposed to improve the sampling efficiency of stochastic gradient MCMC, based on Hamiltonian Monte Carlo.
Stochastic Gradient MCMC with Stale Gradients
In this paper we develop theory to show that while the bias and MSE of an SG-MCMC algorithm depend on the staleness of stochastic gradients, its estimation variance (relative to the expected estimate, based on a prescribed number of samples) is independent of it.
Combining Fully Convolutional and Recurrent Neural Networks for 3D Biomedical Image Segmentation
Segmentation of 3D images is a fundamental problem in biomedical image analysis.
Towards Unifying Hamiltonian Monte Carlo and Slice Sampling
We unify slice sampling and Hamiltonian Monte Carlo (HMC) sampling, demonstrating their connection via the Hamiltonian-Jacobi equation from Hamiltonian mechanics.
Learning a Hybrid Architecture for Sequence Regression and Annotation
When learning a hidden Markov model (HMM), sequen- tial observations can often be complemented by real-valued summary response variables generated from the path of hid- den states.
