rom1504

https://wandb.ai/rom1504/dalle2_train_decoder/runs/mic5buox/files/decoder_config.json

get dalle2

get the config file

get these 2 .sh

run sbatch start_big.sh

bonlime

#!/bin/bash
#
# script to fully prepare ImageNet dataset

## 1. Download the data
# get ILSVRC2012_img_val.tar (about 6.3 GB). MD5: 29b22e2961454d5413ddabcf34fc5622
# wget https://image-net.org/data/ILSVRC/2012/ILSVRC2012_img_val.tar
# get ILSVRC2012_img_train.tar (about 138 GB). MD5: 1d675b47d978889d74fa0da5fadfb00e
# wget https://image-net.org/data/ILSVRC/2012/ILSVRC2012_img_train.tar

tgsmith61591

# -*- coding: utf-8 -*-
#
# Author: Taylor G Smith
#
# Recommender system ranking metrics derived from Spark source for use with
# Python-based recommender libraries (i.e., implicit, 
# http://github.com/benfred/implicit/). These metrics are derived from the
# original Spark Scala source code for recommender metrics.
# https://github.com/apache/spark/blob/master/mllib/src/main/scala/org/apache/spark/mllib/evaluation/RankingMetrics.scala

scapegoat06

Extentions of Topic Models

There are a lot of Topic Models. 18/02/23 # of TM is 24

A

• ATM(author topic model)
  • The Author-Topic Model for Authors and Documents (UAI'04)

kumarbhrgv

Improvising diversity of personalized recommendation systems

Recent Research papers:

• Improving Aggregate Recommendation Diversity Using Ranking-Based Techniques:

  107 Citations : IEEE Transactions on Knowledge and Data Engineering
  we introduce and explore a number of item ranking techniques that can generate substantially more diverse recommendations across all users while maintaining comparable levels of recommendation accuracy. Comprehensive empirical evaluation consistently shows the diversity gains of the proposed techniques using several real-world rating data sets and different rating prediction algorithms

• Recommendation Diversification Using Explanations: (Data Engineering, 2009. ICDE '09. IEEE 25th International Conference)

Traditionally, the problem is addressed through attribute-based diversification grouping items in the result set that share many common attributes (e.g., genre for movies) and selecting only a limited number of items from each group. It is, however,

hal3

"""
PyTorch implementation of a sequence labeler (POS taggger).

Basic architecture:
 - take words
 - run though bidirectional GRU
 - predict labels one word at a time (left to right), using a recurrent neural network "decoder"

The decoder updates hidden state based on:
 - most recent word

dirko

# Keras==1.0.6
import numpy as np
from keras.models import Sequential
from keras.layers.recurrent import LSTM
from keras.layers.core import TimeDistributedDense, Activation
from keras.preprocessing.sequence import pad_sequences
from keras.layers.embeddings import Embedding
from sklearn.cross_validation import train_test_split
from sklearn.metrics import confusion_matrix, accuracy_score, precision_recall_fscore_support

udibr

import numpy as np
from keras.layers import GRU, initializations, K
from collections import OrderedDict

class GRULN(GRU):
    '''Gated Recurrent Unit with Layer Normalization
    Current impelemtation only works with consume_less = 'gpu' which is already
    set.

    # Arguments

shagunsodhani

Key-Value Memory Networks for Directly Reading Documents

Introduction

• Knowledge Bases (KBs) are effective tools for Question Answering (QA) but are often too restrictive (due to fixed schema) and too sparse (due to limitations of Information Extraction (IE) systems).
• The paper proposes Key-Value Memory Networks, a neural network architecture based on Memory Networks that can leverage both KBs and raw data for QA.
• The paper also introduces MOVIEQA, a new QA dataset that can be answered by a perfect KB, by Wikipedia pages and by an imperfect KB obtained using IE techniques thereby allowing a comparison between systems using any of the three sources.
• Link to the paper.

Related Work

anj1

import types
import tensorflow as tf 
import numpy as np

# Expressions are represented as lists of lists,
# in lisp style -- the symbol name is the head (first element)
# of the list, and the arguments follow.

# add an expression to an expression list, recursively if necessary.
def add_expr_to_list(exprlist, expr):